Can Berk Kalayci was born in Denizli, Turkey on June 13, 1982. He received his BS degree from the Department of Computer Engineering, Sakarya University, Sakarya, Turkey in 2005. He received his MS degree from the Department of Industrial Engineering (IE), Pamukkale University (PAU), Denizli, Turkey in 2008. He was awarded a full PhD scholarship to study abroad by Turkish Higher Education Council in 2008. Upon the receipt of his award, he joined Mechanical and Industrial Engineering Department at Northeastern University (NEU), Boston, MA, in January 2009. He has been employed as a research assistant at PAU IE since December 2005. His research interests include environmentally conscious manufacturing and product recovery, disassembly systems, disassembly and assembly line balancing, scheduling and combinatorial optimization. He has co-authored several papers published in conference proceedings, books and journals.
Onder Ondemir was born in Ankara, Turkey in April 1980. He received his BS degree from The Department of Industrial Engineering (IE), Yildiz Technical University (YTU), Istanbul, Turkey, in 2002. He received his MS degree and started his PhD education at YTU IE department in 2004. He had done all-but-dissertation in his PhD studies when he was awarded a full PhD scholarship to study abroad by Turkish Higher Education Council in 2006. Upon the receipt of his award, he joined Mechanical and Industrial Engineering (MIE) department at Northeastern University (NU), Boston, MA in September 2006. He has been employed as a research assistant at YTU IE since April 2003. This dissertation partially fulfills the requirements for the Doctor of Philosophy degree at NU MIE department. His research interests include environmental conscious manufacturing and product recovery, disassembly systems, remanufacturing and production planning, scheduling, mathematical programming, and combinatorial optimization. He co-authored several papers published in conference proceedings, books and respected journals.
Mehmet Ali ILGIN was born in Muğla, Turkey, in 1981. He received his BS degree with highest honours from the Industrial Engineering Department of Dokuz Eylul University, Izmir, Turkey, in July 2003. In August 2006, he received his MS degree in Industrial Engineering from the same university with a GPA of 4.00. Being awarded with a full scholarship by the Turkish Council of Higher Education (YÖK), he started his PhD studies in Industrial Engineering at Northeastern University, Boston, USA in January 2007. He has been employed as a research assistant by Dokuz Eylul University since December 2003.
His research interests are in the areas of closed-loop and reverse supply chains, remanufacturing, disassembly, spare parts inventory management and simulation analysis. He has co-authored several technical papers presented at various national and international conferences and published in their respective proceedings. He has published a number of research papers in refereed international journals such as International Journal of Advanced Manufacturing Technology, Journal of Environmental Management and Resources, Conservation & Recycling. He is listed in Who’s Who in the World and Who’s Who in America.
Badr O. Johar was born in Jeddah, Saudi Arabia in 1979. In 1997, He graduated from Al-Anjal Private School in Jeddah, and then decided to pursue his higher education in the United States. He moved to Boston in January 1998. He received his Bachelor of Science degree in Industrial Engineering with honor from Northeastern University in Boston, Massachusetts in 2002 with a QPA of 3.73. In 2004, he received his Master of Science degree in Engineering Management from the same institution with a QPA of 3.85. He then started a graduate program in Mechanical and Industrial Engineering in Northeastern University in the fall of 2004, and became a PhD candidate in 2006, at the same time he has been a research associate at the Laboratory of Responsible Manufacturing (LRM). His research interests are in the areas of disassembly, product recovery, and production and inventory control. He has co-authored several technical papers presented at various national and international conferences and published in their respective proceedings. Due to his outstanding academic achievements, he has been elected to The National Society of Collegiate Scholars, The Golden Key Honor Society, and The National Honor Society for Industrial Engineering Students Alpha pi mu. Also, he is a member of Institute of Industrial Engineers (IIE) and the Saudi Council of Engineers (SCE). He also received many awards for his academic excellence over the course of his study at Northeastern University. In 2009, alongside with completing his thesis writing, he was appointed as Projects and Planning Manager at Yanbu Cement Company, a leader in cement manufacturing in the Middle East to oversee a new state-of-the-art expansion project (YC5) a 10,000 tons per day plant in collaboration with Europeans and Chinese firms with a total cost exceeding half a billion US dollars. He still hold that position to date.
Amre Massoud was born in London, United Kingdom, in November 1979. He received his B.S. degree from the System and Industrial Engineering Department of the University of Virginia, Charlottesville, Virginia, USA, in May 2002. In December 2003, he received his M.S. degree in Engineering Management from the University of Southern California, Los Angeles, California, USA. He started his Ph.D. studies at Arizona State University, Tempe, Arizona, USA, in September 2004. In September 2005, he transferred to Northeastern University, Boston, Massachusetts, USA, to become a doctoral candidate and a research associate in the Laboratory for Responsible Manufacturing. This dissertation fulfills the requirements for the Doctor of Philosophy degree in the Mechanical and Industrial Engineering department at Northeastern University. His research focuses in the areas of environmentally conscious manufacturing, product recovery, disassembly systems, disassembly process planning, and multiple criteria decision making. He is a co-author of several technical papers presented at various conferences which have been published in their respective proceedings. He is a member of Theta Tau, the nation’s oldest and largest Fraternity for Engineers, and Alpha Pi Mu, the honor society dedicated to the outstanding contribution to education in the field of Industrial Engineering.
Gun Udomsawat was born in Bangkok, Thailand in 1974. He received his B.Eng degree in Civil Engineering from King Mongkut’s Institute of Technology Thonburi, Bangkok, Thailand in 1996. In 1999, he received his MS degree in Engineering Management from Syracuse University, New York, USA with a GPA of 3.97. During his study at Syracuse University, he was awarded a grant from the Association of International Educator (NAFSA). He started the graduate program in Mechanical and Industrial Engineering at Northeastern University, Boston, Massachusetts, USA in 2000. At the same time, he has been a research associate at the Laboratory for Responsible Manufacturing (LRM). He has also been a teaching assistant for several classes including Advanced Production Analysis, Operations Management, Engineering Economy, Probability and Statistics, Engineering Design, and Financial and Project Management. His research interests are in the areas of product recovery, disassembly system, production planning and scheduling, and Just-In-Time (JIT) manufacturing system. He has co-authored several technical papers presented at various national and international conferences and published in their respective proceedings. Due to his outstanding academic and research contribution, he has been elected to The National Honor Society for Academic Excellence, Phi Kappa Phi and The National Honor Society for Industrial Engineering Student, Alpha Pi Mu. He is also listed in the Who's Who Among Students in American Universities and Colleges.
Prasit Imtanavanich was born in Bangkok, Thailand, in 1980. He graduated from Triam Udom Suksa School, Bangkok, Thailand, in 1997. He received his B.S. in Electrical Engineering from King Mongkhut’s Institute of Technology Ladkrabang, Bangkok, Thailand, in 2001. He received his Master’s degree in Industrial Engineering from Northeastern University, Boston, Massachusetts, USA, in 2003. He has been a doctoral candidate and a research associate in the Laboratory for Responsible Manufacturing at Northeastern University since September 2004. His research focuses in the area of environmentally conscious manufacturing which includes product recovery, disassembly process planning, scheduling and optimizing, as well as multiple criteria decision making. He is a co-authored of several technical papers presented at various national and international conferences which have been published in their respective proceedings. He is also elected as a member of Alpha Pi Mu, the honor society dedicated to the outstanding contribution to education in the field of Industrial Engineering, and Phi Kappa Phi, the US's oldest and largest collegiate honor society dedicated to the recognition and promotion of academic excellence in all disciplines.
Srikanth Vadde was born in Vijaywada, Andhra Pradesh, India on the 10th of March 1978. He completed schooling in Hyderabad in 1995 with the highest honors and in the same year joined the Chaitanya Bharati Institute of Technology for his undergraduate studies in Mechanical Engineering. In 1999 he received his Bachelor of Engineering degree with distinction. Then in the year 2000 he came to Boston to pursue his graduate studies at Northeastern University. He was awarded the Master of Science degree in Industrial Engineering in September 2002. As part of his Masters thesis he worked in the area of free-form modeling and developed a registration algorithm to align multiple range images. He joined the doctoral program in industrial engineering in September 2002. In the course of the doctoral program, his work resulted in 15 high quality technical papers which were presented at various conferences and submitted to prestigious journals. In 2002, his outstanding GPA of 3.88 in Masters earned him a membership in Alpha Pi Mu, an honorable industrial engineering society. He was an invited participant for the Doctoral Consortium held at the 2003 Richard Tapia Celebration of Diversity in Computing Conference, Atlanta. In 2003, he received the Outstanding Research Associate Award from the Laboratory for Responsible Manufacturing. For his excellent record as a teaching assistant he was honored with the Outstanding Teaching Assistant Award in 2006 from the College of Engineering. His research paper presented at the 2006 IEEE International Symposium on Electronics and Environment was awarded an honorable mention in the student paper contest. For his outstanding academic record and research contributions, he was inducted as a member into the Phi Kappa Phi Honor Society in the 2007.
Satish Nukala received a Bachelor of Technology degree (with distinction) from the Department of Mining Engineering at the University College of Engineering, Kakatiya University in Kothagudem (India), in May 1998. During his B.Tech program, he worked as an intern at several opencast and underground metal and coal mines that include the Noamundi Iron Mines of Tata Iron and Steel Company Limited (Jharkhand, India); The Singareni Collieries Company Limited (Andhra Pradesh, India) etc. In August 2001, he received a Master of Science in engineering degree from the Department of Industrial and Systems Engineering at the University of Alabama in Huntsville (USA). During the course of his doctoral studies, Mr. Nukala was elected to the national honor societies, Phi Kappa Phi (All Academic Disciplines – American Honor Society) and Alpha Phi Mu (Industrial Engineering – American Honor Society). He also published numerous technical papers in several international conference proceedings, journals and books; and presented his work at various international conferences. As a part time activity during summers, he serves as an official umpire in the Massachusetts State Cricket League. Mr. Nukala’s research interests are in the areas of Logistics and Supply Chain Management, Production Planning and Inventory Control and Multi-Criteria Decision Making Techniques.
Seamus McGovern earned his B.S. degree from Providence College in 1986 and his M.S. degree in Systems and Control Engineering from the University of West Florida in 1994. Serving as a Naval officer from 1986 through 1995, he completed assignments as an operational Helicopter Aircraft Commander, Instructor Pilot, and flight test duty as chief Functional Check Pilot. Subsequently assigned in the Naval Reserves to the Naval Research Lab through 1998 and then the Nat’l Guard as an Attack Helicopter Pilot, Medevac Pilot, and Maint. Test Pilot (including 18 months on active duty commanding a specially trained aircrew in support of Operation Noble Eagle). After leaving active duty in 1995, he was employed as an Electronics Engineer at the Naval Surface Warfare Center with a detail to the Naval Sea Systems Command. In 1997 he accepted his current position as a senior-level Electronics Engineer at the National Transportation Systems Center. Since 2000 he has been a Doctoral Candidate and Laboratory for Responsible Manufacturing Research Associate in the Department of Mechanical and Industrial Engineering at Northeastern University under disassembly pioneer Professor Surendra Gupta with research interests including product recovery, multiple-criteria decision making, algorithms/heuristics/metaheuristics/hybrids, disassembly, and combinatorial optimization. He has co-authored 13 technical papers presented at academic conferences, as well as three accepted journal papers and one book chapter. He is the recipient of four competitive fellowships and 11 merit-based scholarships. He is listed in Who’s Who Among Students in American Universities & Colleges and is an elected member of the Industrial Engineering Honor Society and the Phi Kappa Phi Honor Society.
Lerpong Jarupan was born in Phuket, Thailand in 1972. After being awarded a BS degree in Packaging Technology from Kasetsart University, Bangkok, Thailand in 1994, he worked for Siam Toppan Packaging Company, Samutprakarn, Thailand, and gained industrial experience in the areas of packaging and printing. In 1997, Lerpong was granted a full scholarship by the Royal Thai Government to pursue his graduate studies in the USA and received an MS degree in Mechanical Engineering from Syracuse University. He joined the PhD program in the Department of Mechanical, Industrial and Manufacturing Engineering at Northeastern University in 2000 and was made a member of the Laboratory for Responsible Manufacturing (LRM). His research interests are focused in the areas of packaging and engineering. Lerpong is currently serving as an instructor in the Packaging Technology Department at Kasetsart University in Bangkok, Thailand.
Kishore K. Pochampally received a B.E. degree (with distinction) from the Department of Mechanical Engineering at Regional Engineering College (now - National Institute of Technology) in Bhopal (India), in June 1999. During his B.E. program, he worked as an intern at Hindustan Machine Tools in Hyderabad (India). In June 2002, he received an M.S. degree from the Department of Mechanical and Industrial Engineering at Northeastern University in Boston (USA). While working with his Ph.D. advisor, Dr. Surendra M. Gupta, Mr. Pochampally produced more than 20 technical papers for publication in reputed journals, books, and conference proceedings. In recognition of his "Superior Research and Future Promise", in May 2003, the Laboratory for Responsible Manufacturing (LRM) at Northeastern University presented to him, the Award for Outstanding Scholarly Work. Also, in May 2004, he received the Best Paper Award from the Institute of Electrical and Electronics Engineers (IEEE), at the International Symposium on Electronics and the Environment. He is an elected member of Alpha Pi Mu (Industrial Engineering - American Honor Society) and of Phi Kappa Phi (All Academic Disciplines - American Honor Society). Throughout his academic life, he received numerous merit prizes and scholarships. Mr. Pochampally’s research interests are in the areas of Supply Chain Design & Management, Production/Operations Management, Closed-loop Production Issues, and Decision Sciences
Hasan Kivanç Aksoy was born in Ankara in 1970. He received his BS degree from the Statistics Department of Middle East Technical University (METU), Ankara, Turkey, in June 1993. In March 1994, he was awarded full scholarship for graduate studies in the USA by the Turkish Higher Education Council and appointed to Osmangazi University as a research assistant. In January 1995, he was admitted to the Operations Research MS program in the department of Industrial Engineering and Information Systems (now Mechanical, Industrial and Manufacturing Engineering (MIME)) at Northeastern University, Boston, Massachusetts, USA. He graduated from this program in September 1996 and continued towards his doctoral degree in the same field. He started to work with Prof. S. M. Gupta in the Laboratory for Responsible Manufacturing (LRM). He has co-authored several technical papers with Prof. S. M. Gupta, which have been presented at various national and international conferences and published in their respective proceedings. His research interests are in the areas of inventory control, combinatorial problems, algorithmic models, stochastic processes, optimization and applied probability. He is a member of Sigma Xi, The Scientific Research Society and Alpha Pi Mu, The Industrial Engineering Honor Society.
Elif Kongar was born in Ankara, Turkey, in December 1974. She received her BS degree from the Industrial Engineering Department of Yildiz Technical University, Istanbul, Turkey, in June 1995. In June 1997, she received her MS degree in Industrial Engineering from the same university. In 1998, she was awarded full scholarship for graduate studies in the USA by the Department of Industrial Engineering of Yildiz Technical University, Istanbul, Turkey. She started the graduate program in Mechanical, Industrial and Manufacturing Engineering at Northeastern University, Boston, Massachusetts, USA in September 1998. She has been employed as a research assistant by Yildiz Technical University since November 1996. She has also been a research associate in the Laboratory for Responsible Manufacturing (LRM) at Northeastern University since September 1999. Her research interests include the areas of product recovery, disassembly systems, production planning and scheduling and multiple criteria decision making. She has co-authored several technical papers presented at various national and international conferences and published in their respective proceedings. She is a member of the Scientific Research Society, Sigma Xi, the Industrial Engineering Honor Society, Alpha Pi Mu, the Phi Beta Delta Honor Society and the Phi Kappa Phi Honor Society.
Aybek Korugan was born in Istanbul in May 1969 and was raised there. He was accepted to IEL, a special high school with bilingual education and science concentration, after he ranked in top 50 at the nationwide admittance exams. After graduating from IEL he was accepted to the Industrial Engineering Program at the School of Business Administration of Istanbul Technical University. After graduation, he joined the staff at the IE Department as a teaching and research assistant. He worked as an instructor and as a tutor for various courses in the field. He took part in research and consulting projects that were run by the department.
In June 1993 he was awarded a scholarship for his graduate studies in the USA. In September 1993 he was admitted to the Industrial Engineering MS Program of the Graduate School of Engineering in Northeastern University. He graduated from this program in June 1997 with a 3.81 GPA. Then he was accepted to the Operations Research Program of the same school and started to work with Prof. S. M. Gupta at the Laboratory for Responsible Manufacturing (LRM). Under his supervision he attended several conferences and published papers. In June 2001 he received the "Outstanding Research Associate" Award of LRM.
He is member of the National Industrial Engineering Honor Society (Alpha Pi Mu), the Scientific Research Society (Sigma Xi) and the Institute for Operations Research and the Management Sciences (INFORMS).
Jane E. Boon was born in Ottawa, Canada in August 1967. She was enrolled in a French Immersion program, and pursued a bilingual education through high school. She graduated from Lisgar Collegiate Institute as an Ontario Scholar, Ottawa Board of Education Academic Medallist and as the recipient of a scholarship from the Independent Order of Foresters. She also received a sponsorship from Magna International Inc. of Markham, Ontario to attend GMI Engineering Management Institute in Flint Michigan, where she pursued a BS in Manufacturing Systems Engineering while working as a management intern in a variety of Magna International facilities. Her thesis, which evaluated market opportunities for Magna in the field of alternative fuel vehicles, represented the birthplace for her interest in the societal impacts of technology. Upon graduating Cum Laude from GMI, she was awarded a Keil Fellowship for the "Wiser Uses of Science and Technology" to attend the Massachusetts Institute of Technology. While at MIT, her interests in using quantitative tools to evaluate industrial and public policy issues were fostered, with Dr. Michael Cusumano of the Sloan School serving as her thesis supervisor. She received her MS in Technology and Policy in 1992.
Upon graduating from MIT, she first worked at Procter & Gamble in product development in Toronto, where she undertook the research and built the business case necessary to bring a new product to market. After P&G, she undertook a variety of consulting engagements for smaller companies where her product development and marketing expertise could be leveraged. Her clients included: Ford, Schneider, Safety 1st, Gloucester Public Schools, The Kenan Group, Argyle Investments, Acedemic Resource Group. In addition, she employed her quantitative skills and familiarity with emerging technologies as a stock trader and as a research analyst for a money management firm. In January 2001, she began working for Catalyst International, a software firm that does supply chain execution, as an industry analyst and she currently advises the company on strategic and tactical issues regarding product marketing and development.
In 1997, she enrolled in the doctoral program for industrial engineering at Northeastern University. Her research interests include: technical cost modeling, life cycle analysis, reverse logistics, applied operations research. She has co-authored several technical papers presented a national and international conferences, and has had research papers published and accepted for publication by various journals.
Raymond Wai-Tak Mak was born in Hong Kong in 1966. He attended Northeastern University in Boston, Massachusetts where he received several co-op positions as engineering intern at Massachusetts Bay Transportation Authority, IMEC Corporation, and Thinking Machines Corporation and earned his B.S. degree (1991, summa cum laude) and M.S. degree (1993) in Industrial Engineering.
From 1991 through 1993 he received an Assistantship position in the Industrial Engineering and Information Systems Department (now Mechanical, Industrial, and Manufacturing Engineering) at Northeastern University and worked as a research assistant on concurrent engineering framework design for electronics manufacturing using simulation tool and artificial intelligence methods. He also served as a teaching assistant for undergraduate engineering economy and operations research courses.
After completing the required course work for his Ph.D. in 1994, he worked as a research assistant in the Department of Management Sciences at City University of Hong Kong. His research interests are in the areas of production planning and scheduling, quality management and business process re-engineering. He has co-authored several research and technical papers that are published in various journals and technical reports. He has worked as a Development Engineer for development of knowledge-based simulation system and constraint-based scheduling system at Resource Technologies Limited in Hong Kong. In 1998, he joined FedEx Express as Technical Business Advisor in their divisional Planning and Engineering department in Hong Kong. He defended his dissertation for the Doctor of Philosophy degree in Industrial Engineering with specialization in Manufacturing Systems in June 2001. His research findings have been submitted to refereed journals; of these one has been published and one has been accepted for publication.
He is a senior member of Institute of Industrial Engineers, a member of Order of the Engineer, Tau Beta Pi (the National Engineering Honor Society), and Alpha Pi Mu (the Industrial Engineering Honor Society). He is also listed in Who's Who Among Students in American Universities and Colleges.
Askiner Güngör was born in Artvin, Turkey, in August 1971. He graduated with first rank from elementary, junior and high schools. He received a BS degree with honors from the Industrial Engineering Department of Gazi University, Ankara, Turkey, in June 1992. In 1993, he was awarded full scholarship for graduate studies in the USA by the Department of Industrial Engineering of Pamukkale University, Denizli, Turkey. He entered the graduate program in Industrial Engineering and Information Systems (now Mechanical, Industrial, and Manufacturing Engineering) at Northeastern University, Boston, Massachusetts, USA in September 1993. He was awarded the MS degree in September 1996.
He worked as an engineering intern at Vestel Elektronik A.S. (a company producing brown and white goods), Manisa, Turkey, in Summer 1991 and at Ozkul Tekstil Ltd. Sti. (a textile company), Izmir, Turkey, in Summer 1992. He worked as a research assistant at Department of Industrial Engineering of Gazi University, Ankara, Turkey. He has been a research associate in the Laboratory for Responsible Manufacturing at Northeastern University in Boston, Massachusetts since October 1996. His research interests are in the areas of scheduling, combinatorial problems, algorithmic models, graph theory, and remanufacturing/disassembly systems. He has co-authored several technical papers presented at various national and international conferences and published in proceedings of the respected conferences. Several of his research papers have been accepted or are being considered for publication by various journals.
He is a member of Sigma Xi, The Scientific Research Society and Alpha Pi Mu, The Industrial Engineering Honor Society.
Pitipong Veerakamolmal is a research associate in the Laboratory for Responsible Manufacturing at Northeastern University in Boston, Massachusetts. His educational background includes a BS degree in Computer Engineering from King Monkut's Institute of Technology (Ladkrabang), Bangkok, Thailand, and an MS degree in Industrial and Manufacturing Engineering from Northeastern University, Boston, Massachusetts, U.S.A. He is expected to get his Ph.D. degree from Northeastern University in June 1999. He has worked as an Industrial Process Control Systems Engineer for cement production and pulp and paper production industries in Thailand. His current research interest within the Environmentally Conscious Manufacturing includes cost-benefit analysis in designing products for the environment, integration of product take-back systems, bill of materials (BOM) information systems for products disassembly, economic analysis and lot-sizing for multiple products disassembly, and planning and scheduling of re-manufacturing systems. He specializes in the theory of scheduling and sequencing, linear programming, and simulation. He has published his research findings in over fifteen articles in journals, book chapters, conference proceedings; and presented his work at numerous conferences, both in the US and abroad.
Kendra Elaine Moore was born in Dayton, Ohio, in 1959, and was raised in Richmond and Indianapolis, Indiana, Normal, Illinois, St. Louis, Missouri, and Minneapolis, Minnesota. She graduated from Blaine Senior High School, valedictorian, and attended Stephens College in Columbia, Missouri, on a full academic scholarship, which included a year of study at Oxford University in England. She was awarded the BA, magna cum laude, in 1981 with a dual major in Philosophy and Religion and Women's Studies, and a minor in mathematics. She attended Columbia University in New York City and was awarded the MA in Philosophy of Religion in 1985.
Upon completing the MA degree, she joined the technical staff at ALPHATECH, Inc., in Burlington, Massachusetts. At ALPHATECH, she has worked as a Member of the Technical Staff, task leader, project leader, project manager, and product development manager. Her areas of expertise include large-scale and discrete-event systems modeling, command and control systems, performance modeling, Petri net modeling, simulation, and human engineering. In 1993, she received the first annual ALPHATECH Entrepreneur of the Year award. She is currently the Manager for Human Engineering and Simulation, in the Information and Decision Sciences Division at ALPHATECH. Her responsibilities include technical and financial project management, new and follow-on business development, marketing and proposal writing, and staff supervision.
In 1986 she entered the MS program in Operations Research in the Industrial Engineering and Information Systems Department (now Mechanical, Industrial, and Manufacturing Engineering) at Northeastern University as a part-time student. She was awarded the MS degree in 1989; the title of her master's thesis was "Parallel Implementations of the Auction Algorithm". She entered the Ph.D. program as a part-time student in 1990 and completed additional course work in 1993. In January of 1996, she became a full-time student at Northeastern while working part-time at ALPHATECH.
Immediately upon completing the Ph.D., she will return to full-time status at ALPHATECH, as Associate Division Manager for Decision Systems. She has submitted three papers to refereed journals; of these one has been published and one has been accepted for publication.
Leanne was born in Landsdale, Pennsylvania in 1969 and was raised in North Kingstown, Rhode Island. She completed one year at Cornell University, with honors, and then transferred to Northeastern University where she also received her first co-op position in the Industrial Engineering (I.E.) Department at United Parcel Service in 1989. She continued in various I.E. positions, alternating as a part-time employee when taking classes, while she earned her B.S. (1991, summa cum laude) and M.S. (1992) in Industrial Engineering.
From 1991 through 1994 she received an Assistantship position in the Industrial Engineering and Information Systems Department (now Mechanical, Industrial and Manufacturing Engineering) at Northeastern. Over this time period she worked as a research assistant on a variety of projects ranging from knowledge-based risk assessment models to simulation analysis tool design using an expert systems approach. She also served as a teaching assistant for an undergraduate simulation course, and as an instructor for a course in computer methods in manufacturing.
After completing the required course work for her Ph.D. in 1995, she took a position at United Parcel Service in their Research and Development department in Danbury Connecticut. She defended her dissertation for the Doctor of Philosophy degree in Industrial Engineering with a specialization in Manufacturing Systems in September 1996.
She transferred to Atlanta, Georgia to work as an Industrial Engineer in the Corporate Plant Engineering Automation Department for United Parcel Service. Her plans are to continue in academia on a part-time teaching/research status while pursuing her career in industry. She has published several papers and intends to continue research in the areas of manufacturing, automation, and environmentally conscious industrial practices.
Fikri obtained his B.S. degree in Industrial Engineering from Middle East Technical University in Turkey and an M.S. and PhD degrees in Industrial Engineering from Northeastern University. His research interests are in the area of applied probability and optimization. His work has been published in several journals and conference proceedings. He is currently working in Paris, France.
Ayse obtained her B.S. degrees in Chemistry and Industrial Engineering from Purdue University and an M.S. and PhD degrees in Industrial Engineering from Northeastern University. Her research interests are in the areas of Production Systems and Operations Research. She is currently working for the IBM corporation.
Karim N. Taleb is a Cornell University graduate who received a Bachelors Degree in Operations Research in May 1989 (Dean’s List) and a Masters of Engineering Degree in May 1990. Karim also completed a Manufacturing Engineering Certificate during his graduate studies at Cornell. In June 1990, he started a venture with two other entrepreneurs in Boston, and later on decided to extend his academic training at Northeastern University.
A staunch environmentalist and a long-time member of environmental organizations, Karim pledged early on to dedicate his time and effort to the environmental cause. He identified disassembly manufacturing as his research interest, and in which he foresaw an inevitability yet to manifest itself. At the time, the industrial and academic efforts were focused on one-way production processes with little attention to their externalities and heavy environmental impact.
Still dormant in its embryonic state, disassembly represented a paradigm-shift that would carry a broad and deep impact on the established architecture supporting the production and consumption processes. Karim's dissertation (Operational Issues in Disassembly) identified this impending change and raised the awareness and pressing need for academic and industry action. The dissertation provides a concise assessment of some strategic and operational implications and lays out early versions of algorithms and novel heuristics that could be used to support the material management processes.
Dr. Taleb published several refereed papers and contributed to national and international conferences. His research proved to be timely and pioneering and eventually led to a solid contribution in the field. He obtained a Doctor of Philosophy in June of 1995.
Yousef obtained a BS degree in Industrial Engineering from University of Portland, and MS degree in Mechanical Engineering from King Saud University, and an MS in Engineering Management and a PhD in Industrial Engineering from Northeastern University. He is currently an assistant professor of research and Assistant Director of the Technology Department at King Abdulaziz City for Science and Technology. His research interests are in the area of applied production systems and technology transfer. He is a member of IIE, INFORMS and DSI.
Recent technological advancements have given birth to the growth in electronics and the availability of inexpensive products with high quality. Due to human nature, consumers constantly look for newer products although what they have may be still functional. Functional or not, used products are routinely discarded. This trend has led to extreme depletion of virgin resources to satisfy the ever increasing demands of the customers. In addition, the waste created by products reaching the end of their useful lives pose challenges for the environment. Environmental and economic concerns together with the stricter government regulations and public awareness on the disposal of end-of-life (EOL) products have led to the concept of “Product Recovery”. Product recovery seeks to obtain materials and parts from old or outdated products through recycling and remanufacturing in order to eliminate environmental negative impact in a cost effective manner.
The first crucial and the most time consuming step of product recovery is disassembly. Disassembly is defined as the systematic extraction of valuable parts and materials from discarded products to be used in remanufacturing or recycling after appropriate cleaning and testing operations. Disassembly operations can be performed at a single workstation, in a disassembly cell or on a disassembly line. Just as assembly line is considered to be the most efficient way to assemble a product, the disassembly line is the most efficient way to disassemble a product. Disassembly operations have unique characteristics and cannot be considered as the reverse of assembly operations. The quality and quantity of components used in the stations of an assembly line can be controlled by imposing strict conditions. However, there are no such conditions on EOL products moving on a disassembly line. In a disassembly environment, the flow process is divergent; a single product is broken down into many subassemblies and parts while the flow process is convergent in an assembly environment. There is also a high degree of uncertainty in the structure, quality, reliability and the condition of the returned products in disassembly. Additionally, some parts of the product may be hazardous and may require special handling that will affect the utilization of disassembly workstations. Since disassembly tends to be expensive, disassembly line balancing becomes significant in minimizing resources invested in disassembly and maximizing the level of automation.
The multi-objective Disassembly Line Balancing Problem (DLBP) seeks to obtain a feasible disassembly solution sequence while minimizing the number of workstations, minimizing the total idle time, ensuring similar idle times at each workstation as well as addressing other disassembly specific concerns such as demand criteria and handling hazardous components. DLBP is sequence-dependent in nature and therefore it is required to consider sequence-dependent setup times in addition to standard part removal times for a more realistic scenario. In this dissertation, DLBP which has been proved to be NP-complete, is extended to Sequence-Dependent Disassembly Line Balancing Problem (SDDLBP) and mathematically formalized. By setting all sequence-dependent time increments to zero, SDDLBP reduces to DLBP. Therefore, SDDLBP is a generalization of DLBP. Since SDDLBP falls into the NP-Complete class of combinatorial optimization problems, when the problem size increases, the solution space is exponentially increased and an optimal solution in polynomial time cannot be found as it can be time consuming for optimum seeking methods to obtain an optimal solution within this vast search space. Therefore, it is necessary to use alternative methods in order to reach (near) optimal solutions faster. For this reason, fast and effective algorithms such as Ant Colony Optimization (ACO), River Formation Dynamics (RFD), Tabu Search (TS), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Hybrid Genetic Algorithms (HGA) and Artificial Bee Colony (ABC) optimization approaches are modified, developed and implemented to solve SDDLBP. Various scenarios are considered to illustrate the application of each methodology. Quantitative, and qualitative analyses and comparisons are provided. Conclusions drawn include the consistent generation of (near) optimal solutions, the ability to preserve precedence, the speed and the efficiency of the techniques and their practicality for implementation. The various techniques presented in this research form a body of knowledge directed at addressing problems involving disassembly lines.
Recent technological advancements have energized the growth in electronics and thus endowed the customers with high quality yet inexpensive consumer goods. Availability of such products has caused a consumption revolution. In particular, used products are routinely discarded prematurely. In other words, products become obsolete or “old” in a much shorter time although they are still functional. This trend has led to tremendous depletion of virgin resources to satisfy the ever increasing demands of consumers.
Awareness on environmental problems has risen with the distressing increase in the use of virgin resources and has led to several legislations that extend manufacturers’ responsibility beyond the point of sale. New legislations and public awareness have forced manufacturers to form a reverse flow originating from customers in order to eliminate negative impacts of their products on the environment when they reach their end-of-lives (EOL).
We investigate each scenario by carrying out two separate designs of experiments studies based on orthogonal arrays for the cases with and without SEPs. Then, we present the results of pairwise t-tests comparing two cases based on nine performance measures, viz., disassembly cost, disposal cost, backorder cost, holding cost, testing cost, transportation cost, total cost, total revenue and profit.
There are many advantages to EOL management such as reduction in the use of virgin resources, decrease in the use of landfills and cost savings stemming from the reuse of end-of-life products (EOLPs), disassembled components and recycled materials. In general, the management of EOLPs involves a set of operations viz. cleaning, disassembly, sorting, inspection, and recovery or disposal. There are several recovery options that can be selected. These options include product recovery, component recovery (cannibalization), and material recovery (recycling). Product recovery may take several forms such as remanufacturing, refurbishing, repairing and recycling. All recovery options involve disassembly operations up to a certain level. Disassembly is a labor intensive operation carried out to extract parts from EOLPs for several purposes including elimination of hazardous parts, reusable component recovery, component testing, and content inspection. Of all recovery operations, remanufacturing and disassembly are considered to be the most complex ones. This is mostly due to the lack of information about the quality and quantity of EOLPs and their components. When there is no information available on the components’ quality status, comprehensive testing is needed to collect that. After testing, if an EOLP is found not suitable for remanufacturing, the time and resources spent on determining that are wasted, otherwise, necessary remanufacturing operations and spare parts are listed based on the testing results. EOLPs, however, do not show typical qualities since they originate from various sources where they are subjected to different working conditions. As a result, it is highly likely that each EOLP has its own quality condition exhibiting unique remanufacturing needs. Hence, finding the EOLPs with minimal recovery costs requires testing the whole EOLP inventory, which can be very expensive. However, emerging information technology devices, such as sensors and radio-frequency identification (RFID) tags, mitigate EOL recovery decision making by reducing or eliminating uncertainty.
Sensor embedded products (SEPs) are built with sensors implanted in them to monitor their critical components while they are in use. Sensors may be used in addition to the radio frequency identification (RFID) technology that has recently gained importance in closed loop supply chain operations, including reverse logistics, disassembly and remanufacturing, as a means of communication and data storage. Using the information collected by sensors, existence, types, conditions and remaining lives of components in an end-of-life product (EOLP) can be determined. Remaining useful life can be taken into account as a good measure of quality. Therefore, determination of remaining useful life allows decision makers to construct sophisticated recovery models that accommodate remaining life based demands and guarantee a minimum customer satisfaction level on recovered products while optimizing various system criteria.
In this dissertation, we investigate how sensors and RFID tags could be used to assist product recovery operations and propose an advanced remanufacturing-to-order and disassembly-to-order (ARTODTO) system for end-of-life sensor-embedded products (SEPs). Several mathematical models are developed for different recovery scenarios to determine how to process each and every end-of-life product on hand to meet the remaining life based product and component demands as well as recycled material demand while fulfilling various system criteria. Demands are met by disassembly, remanufacturing, and recycling operations. Outside component procurement option is used to eliminate the component and material backorders. Various scenarios are considered to illustrate the application of each proposed methodology. Together these scenarios form a body of knowledge that shed light on the importance of using SEPs in mitigating uncertainties and providing financial and environmental incentives in product recovery.
Environmental and economic concerns together with the stricter government regulations on the disposal of end of life (EOL) products have increased the importance of product recovery. Disassembly is the first operation in product recovery. Due to missing or nonfunctional components, an accurate prediction of the number of components that can be recovered from a product prior to disassembly is very difficult. This highly uncertain nature of disassembly environment is the focus of the research presented in this dissertation. In particular, the use of sensor embedded products (SEPs) to deal with this uncertainty is investigated. SEPs involve sensors embedded in products during the production process. The information provided by these sensors on the version and condition of the components of a product prior to disassembly can provide important savings in disassembly, backorder, holding and disposal costs.
The objective of this research is to provide a quantitative assessment of the impact of SEPs on various performance measures of a disassembly line. We carry out this assessment for several scenarios of the disassembly line operation including a scenario with common products having no precedence relationship, a scenario with common products having one type of precedence relationship, a scenario with products having multiple precedence relationships, a scenario with component discriminating demand, and a scenario with both multiple precedence relationships and component discriminating demand.
We investigate each scenario by carrying out two separate designs of experiments studies based on orthogonal arrays for the cases with and without SEPs. Then, we present the results of pairwise t-tests comparing two cases based on nine performance measures, viz., disassembly cost, disposal cost, backorder cost, holding cost, testing cost, transportation cost, total cost, total revenue and profit.
The various scenarios presented in this dissertation form a body of knowledge that sheds light on the importance of SEPs in combating uncertainties and providing financial and environmental incentives in product recovery.
Recent years have witnessed Reverse Supply Chain (RSC) become the center of attention for researchers and Original Equipment Manufacturers (OEMs). The fast depletion of virgin resources and the rapid increase of product returns from customers to original manufacturers for maintenance, repair or to be disposed of can be said to be one of the main reasons behind this interest. Electronics manufacturers have introduced state-of-the-art technologies in quick succession. As a result of this, the end-of-life (EOL) products returned by customers have grown significantly in volume. But often these EOL products are found to be in excellent working conditions (functional). Customers return them because of the various marketing programs or favorable incentives offered by service providers or manufacturers that create a “must have” sense in the minds of customers to acquire upgraded products. In the past two decades environmental concerns have influenced production processes, as environmental regulations have targeted pollution from industry. However, there is growing awareness that this may not be sufficient and it is increasingly recognized that the use and disposal phases, as well as the production phase of the product life cycle, are important. Environmentalists have always demanded that the manufacturing companies should take these products back and manage them in an environmentally conscious manner. End-of-Life (EOL) products can be remanufactured, reused, recycled, or disposed of. However, manufacturers have not invested or engaged themselves in such initiatives because of the uncertainty associated with the process.
Many corporations have understood the economic and environmental benefits of minimizing the use of virgin resources. Also, due to evolving environmental legislation, they have started to comprehend the importance of the recovery process and are taking serious steps in restructuring their supply chain processes to meet the new regulations. The idea behind the change is to use materials and parts more than once before they are finally discarded. Thus, effective supply chain management is vital in gaining a competitive edge over other corporations. The take-back process is clearly more environment friendly than the traditional forward supply chain process as it “closes the loop” of the supply chain process and transforms the EOL products into new serviceable products. This new portion of the supply chain is known as Reverse Supply Chain. It has been found that the original supplier is in the best position to control the return process. The reverse supply chain logistics model operates independently of the forward supply chain that delivered the original product.
In comparison to regular supply chain management, the management of reverse supply chain is more challenging because it is much more reactive and less visible. A major challenge is inventory control and value management of EOL products. Due to the disparity between demand for parts and materials and their line yields, the decision maker faces economical as well as physical constraints when trying to take a decision on how many products to take back and when to take them back, and when to keep them and for how long before they are finally disposed of.
The objective of this research is to develop inventory control policies of on hand inventory (OHI) of returned products and disassembled parts in such a way that total variable costs of the system is minimized and the profit is maximized, and also to provide management tools that can help improve the overall performance of the disassembly line. These inventory management and planning questions have not been fully addressed in the literature.
In this research, two types of approaches have been presented and tested to model the inventory control problem in context of disassembly. The first type of approaches is deterministic, while the second type of approaches is stochastic. The performances of the two approaches have been tested given the uncertainty of demand, return and product on the total solution cost. The results show that stochastic models outperform the deterministic model when uncertainty levels are high, and provide a better solution.
The various techniques presented in this dissertation provides knowledge that helps in understanding the challenges and opportunities associated with inventory control in a disassembly line context and form a body of knowledge that helps in addressing the inventory problems associated with a disassembly line.
In recent years, the idea of properly managing waste generated from end-of-life (EOL) products has gained a lot of attention. Technology has evolved over the past few years and the cost of technology has been declining at a steady rate making them more readily accessible to a wider range of consumers. As a result, the demand for newer products with higher technologies has surged. This surge in demand along with consumers’ desires to upgrade their products has fueled manufacturing companies to continuously develop new products with superior technologies that would outperform the previous products. Accordingly, consumers were tempted to upgrade or replace their old products. This rush to upgrade older products has led to the premature disposal of older products. Additionally, this phenomenon has dramatically shortened the life cycle of products and led to the early retirement of products before reaching the end of their actual operational/functional life cycle.
Waste generated from EOL electronic products has grown incredibly large over the past few years and become a huge negative burden impacting the wellbeing of the environment. Every year, millions of tons of fully functional electronic products such as televisions, personal computers, and cell phones are being dumped into landfills. Consequently, raw materials are depleting swiftly, pollution and harmful waste is increasing at a high rate, and landfills are filling up rapidly. In order to reduce the waste and minimize its negative impact on the environment, proper management of this waste is crucial. This problem has caught the attention of consumers and governments around the world. New laws and regulation have been established by governments which aim to reduce and control the amount of waste sent to landfills. Additionally, consumers’ awareness along with the new laws and regulations have forced manufacturing companies to become more environmentally conscious. However, in reality, being environmentally consciousness is embraced more by its business value rather than regulations. Thus, it is often welcomed by manufacturers due to the lucrative profits and the potential of projecting a good image in the community.
Manufacturers have thought of different ways to deal with the waste generated from EOL products. They have realized that they have several choices when it comes to managing this waste. Returned EOL products can be remanufactured, reused, recycled, or disposed of. By remanufacturing, reusing, and recycling EOL products, manufacturers reduce their dependency on virgin resources, help decrease the rate of depletion of these virgin resources, and reduce the amount of harmful waste sent to landfills. The disposal of these EOL products is used as a last resort because it increases pollution, harms the environment, and reduces the number of landfills.
Disassembly is often the first crucial step in remanufacturing, reusing, and recycling. In order for manufacturers to disassemble EOL products, they first need to obtain the EOL products through the disassembly process. However, several uncertainties exist in the disassembly process which further complicates the process and makes it more difficult to know in advance the exact number of EOL products needed for disassembly that would fulfill the different demands for components.
This dissertation focuses on the disassembly-to-order (DTO) system where a variety of EOL products are taken back or purchased from the end user for disassembly. EOL products are disassembled into individual components and materials to satisfy the different demands. To solve the problem, a DTO model is developed that take into consideration a number of system uncertainties. We explore various techniques to solve the DTO model. The main objective is to develop a DTO plan that determines the best combination of take-back EOL products to be purchased from each supplier in every period that would fulfill the demand while achieving various financial and environmental goals.
In this dissertation, we use heuristic procedures to handle the product condition uncertainties in the model. The two heuristic procedures “one-to-one” and “one-to-many” heuristics were implemented by Inderfurth and Langella. The heuristic procedures are able to transform the stochastic yields into their equivalent deterministic yields using different system costs. Once the stochastic part of the problem has been converted to its equivalent deterministic, the remainder of DTO problem can be solved using various techniques. We implement five different techniques to solve the DTO problem.
The first technique is Nonlinear Programming (NLP). The NLP technique determines the best combination of take-back EOL products to be purchased from every supplier in every period while trying to maximize total profit. The second technique is Dynamic Programming (DP). This technique is an extension of the previous technique where the main objective is to find the best combination of EOL products to be purchased in every period from each supplier that fulfills the demand of components while maximizing total profit of the system. Both techniques, NLP and DP, aim to satisfy the demand of components in all periods, however, NLP optimizes each period independent of the other periods while DP optimizes the whole system as one. The third technique is Preemptive Goal Programming (PGP). This extends the previous techniques by considering multiple goals but solves the DTO model under the same previous circumstances. The decision maker (DM) prioritizes the different goals based on their importance and defines aspiration level for each goal that needs to be achieved. The main objective is to determine the best combination of EOL products to be purchased from every supplier in every period that satisfies the demand while trying to achieve the aspiration levels for multiple goals. The fourth technique is Weighted Fuzzy Goal Programming (WFGP). This technique is an extension of the PGP technique because weights and vagueness are introduced to the goals of the DTO model. Weights are assigned by the DM to each goal in order to prioritize the goals. Additionally, goals are allowed to be overachieved or underachieved but with lower satisfaction levels. The last technique is Linear Physical Programming (LPP). This technique is a further extension of the previous techniques where no predetermined weights exist for the goals and vagueness exist in the system. In this case, the DM is unfamiliar with the system and/or unable to correctly assign weights to prioritize the goals. Therefore, LPP removes the DM from the process of choosing weights and prioritizing the goals and assigns the weights and prioritizes the goals automatically.
The various techniques presented in this dissertation form a body of knowledge that helps in solving the DTO problem with several system uncertainties under various environmental, financial, and physical constraints.
This thesis focuses on the production control aspect of the disassembly environment. In particular, the kanban control mechanism is adapted and implemented on a disassembly line. The kanban control mechanism was originally designed for an assembly environment. It was, therefore, necessary to adapt the control mechanism to suit the disassembly line environment which is fraught with all kinds of uncertainties and challenges such as arrival of products for disassembly at intermittent workstations, arrival of demands for components at intermittent workstations and uneven fluctuations in inventory levels at intermittent workstations. These characteristics make a disassembly line much more complicated and difficult to control than a traditional assembly line. In an assembly line setting, there are two types of production control systems, viz., push system and pull system. A push system is easy to implement in the disassembly environment but it is not efficient as it tends to generate large amounts of inventory. A pull system using a Kanban control mechanism produces much less inventory but it cannot be implemented in a disassembly line setting in its current form.
The objective of this research is to develop a kanban control premise to be implemented in a disassembly line environment while providing evidence that such implementation would help the system gain production efficiency while minimally sacrificing other performance measures.
To this end, we develop a system that uses several types of kanbans attached to various components and subassemblies. The heart of the system lies in the kanban routing mechanism which allows navigation of kanbans in multi-directions based on real time conditions. The idea is to create minimal amount of residual inventory while satisfying varying demands for components and subassemblies.
We thoroughly investigate several scenarios of the disassembly line operation including a scenario with common products, a scenario with component discriminating demand, a scenario with products having multiple precedence relationships, and a scenario with workstation breakdowns. These scenarios represent various situations that a facility might face when dealing with the disassembly of both single and multiple products on a disassembly line. In each scenario, we examine the effectiveness of the multi-kanban model using three performance measures, viz., the inventory level, the level of satisfied demand, and the customer waiting time. We compare these results with the ones generated from the same line that employs a traditional push system. Using simulation, we demonstrate that the overall performance of the disassembly line using multi-kanban mechanism outperforms the disassembly line with the traditional push system.
The various techniques presented in this research form a body of knowledge that helps understand the complexities and benefits of a better production control mechanism under various physical, operational and environmental constraints.
Managing waste generated from end-of-life (EOL) electronic products has become a priority for many countries. Due to rapid technology development of electronic products, as well as customers’ desires to acquire products with superior technologies, products with inferior technologies are often discarded. This leads to a premature disposition of products that are otherwise in good functioning conditions resulting in shortened life cycles of the products. A proper management of this phenomenon is therefore necessary to prevent and reduce electronics wastes, and hence minimize the negative impact to the environment.
There are several EOL management choices, viz. remanufacturing, reuse, recycling and disposal. Of these, the first three are preferable choices. Disposal, on the other hand, is the least desirable choice as it takes up landfill space and depletes valuable natural resources.
Regardless of the choice made to manage the EOL products, the first step to recover or separate components and materials is disassembly. In this research, we focus on the disassembly-to-order (DTO) system. It is a system where a variety of EOL products are taken back and disassembled in order to fulfill the demands for specified numbers and quantities of components and materials. The objective of this research is to effectively generate a complete DTO plan by considering products’ uncertainties, system requirements and constraints. The plan includes the determination of the optimal numbers of take-back EOL products to be sent to the disassembly station, the parts to be disassembled from each product, and the optimal disassembly sequences of all products under several disassembly scenarios. To this end, heuristic procedures and techniques are employed to manage the stochastic yields caused by products’ uncertainties for solving multi-objective problems for five different disassembly scenarios. A Refining Algorithm is also developed to improve the solutions and generate a complete DTO plan.
We use two heuristic procedures to manage the stochastic disassembly yields of the DTO problem by converting them into their deterministic equivalents. The two procedures are termed as “one-to-one” and “one-to-many” heuristics. Once the procedures are implemented, the remaining deterministic DTO problem is solved by using one of the five techniques as follows.
The first technique is Preemptive Goal Programming. It helps generate the optimal numbers of take-back EOL products to satisfy the demands and attain the financial and environmental aspirations. The second technique is Weighted Fuzzy Goal Programming. It is an extension of the first technique where deliberate vagueness is introduced in the aspiration levels. The third technique is Linear Physical Programming. It is an alternative to the second technique where the procedure automatically determines and assigns priority to the goals. The fourth technique is Genetic Algorithm. It is a heuristic evolutionary technique that continuously improves the solution until the presumably optimal solution is reached. By using this technique, the near-optimal number of take-back EOL products and disassembly sequences can be obtained in a very short time. The fifth technique is Neural Network (NN). By using this technique, the problem is solved distinctively since model formulation is not necessary. This helps eliminate all complexities of building the model, dealing with uncertainties, and satisfying all goals and demands. Only known input-output data is needed to train the NN. After the NN is properly trained, it is able to provide reasonably optimal solutions in an extremely short time.
Once the numbers of take-back EOL products and disassembly schemes are obtained, a Refining Algorithm is applied to improve the disassembly solution and generate a complete DTO plan. The algorithm first analyzes the numbers of take-back EOL products and all corresponding demands, and then refines the disassembly strategy by rearranging the flow of the products and items which help improve the financial and environmental results. Finally the complete DTO plan is summarized and presented.
The various techniques presented in this research form a body of knowledge that can help in solving the DTO problem with multiple objectives, various physical, financial and environmental constraints under several uncertainty situations.
The perpendicular rise in the quantity of discarded products in the recent past can be attributed to the product obsolescence and shorter life span of products. This has led to the enforcement of legislations which hold original equipment manufacturers (OEMs) responsible for their end-of-life products. Many OEMs, even though aware of the economic worth possessed by discarded products, are hesitant to incorporate product take back programs in their supply chains. Encouraged by OEMs disinclination, independent, small-scale, third-party product recovery facilities (PRFs), are taking the discarded products, performing product recovery operations and are sustaining themselves from the revenue earned through the sale of the recovered reusable and recyclable components. However, many PRFs are financially plagued by the costly and labor intense product recovery operations, competition from OEMs and other PRFs, and environmental regulations. These factors apart, PRFs face many challenges: (a) uncertainty in the timing and quantity of discarded products arriving at the facility; (b) fleeting inventory levels of recovered components ensuing from the unpredictable disposal of products and stochastic demand for their components; and (c) disposal cost of leftover and obsolete inventory. It is absolutely critical for PRFs to manage their inventories to report a stable economic growth. An effective way to achieve this is, PRFs proactively adopt pricing and disposal policies that methodically clear inventories. This strategy has a twofold impact: facilitates inventory management and boosts the profit margin.
Currently, many PRFs sell their products without a well defined pricing structure. They usually price their products as a fixed percentage of the price of new products in the market. Moreover, there is scant research literature on pricing models for PRFs, although considerable amount of work addresses pricing issues for OEMs which sell both new and remanufactured products. Hence, there is a need to sew this hole in the fabric of pricing literature. Also, most articles in the disposal literature address issues only for product return inventories and fail to address disposal issues for reusable and recyclable component inventories which could pile up in spite of adopting a clearance-friendly pricing policy. This dissertation develops effective price and disposal decision models for reusable and recyclable components. The models incorporate (a) production and inventory control measures such as inventory replenishment, backordering, and constraints on inventory levels; (b) various pricing mechanisms such as static and dynamic pricing; (c) demand functions which are deterministic and stochastic; (d) discarded product acquisition mechanisms such as passive acceptance, proactive acquisition, and integrated passive and proactive; (e) the environmental regulation on the disposal of products; (f) the effects of product obsolescence, quality, and customer willingness-to-pay on the demand; and (g) competition in the market to acquire discarded products and sell recovered items. Profit and inventory level are the metrics to evaluate the models. The key findings of the models are: (a) the price of components is sensitive to the quantity of returned products, yield, disposal limit, product obsolescence, quality, customer willingness-to-pay, acquisition cost, sorting cost, disassembly cost, remanufacturing and processing cost, holding cost, and disposal cost; (b) the disposed quantity is affected by the quantity of returned products, yield, disposal limit, and holding and disposal costs.
Both consumer and government concerns for the environment are driving many original equipment manufacturers to engage in additional series of activities stemming from the reverse supply chain. As a result, economically feasible production and distribution systems are established that enable remanufacturing of used-products in conjunction with the manufacturing of new products. The combination of forward/traditional supply chain and reverse supply chain forms the closed-loop supply chain. While this process is mandatory in Europe, it is still in its infancy in the United States.
Strategic, Tactical and Operational planning are the three important stages of planning in a Supply Chain. Strategic planning primarily deals with the design (what products should be processed/produced in what facilities etc) of the supply chain that is typically, a long-range planning performed every few years when a supply chain needs to expand its capabilities. Tactical planning involves the optimization of flow of goods and services across the supply chain and is typically a medium-range planning performed on a monthly basis. Finally, Operational planning is a short-range planning that deals with the day-to-day production planning and inventory issues on the factory floor.
Many location models have been reported in the literature for designing forward and reverse supply chains. However, not many models deal with both the forward and reverse supply chains together. While every location model realizes the importance of re-processing an economical used product, procuring that used product from an economically viable supplier and re-processing them in efficient production facilities, not many models address the specific issues of selecting that economical used product from a set of different used products or identify that economically viable supplier from several available suppliers or identify that efficient production facility from several available facilities in the region where the supply chain is to be designed. Every model assumes that each incoming used product is economical enough to re-process in production facilities that are assumed to be efficient or can be set up in locations solved for. As a result, there is a risk of procuring uneconomical used products from infeasible suppliers and re-processing them in inefficient production facilities.
Reverse supply chains differ from forward supply chains in many aspects and are complex to handle because of the inherent uncertainty involved in every stage of their planning. In addition, the quantitative models reported in the literature for designing a forward supply chain do not address the issue of environmental consciousness. As a result, most of the existing forward supply chain models are not suitable to include reverse logistics.
In an era of globalization of markets and business process outsourcing, many firms realize the importance of continuous monitoring of their supply chain’s performance for its effectiveness and efficiency. Due to the inherent differences in various aspects between the forward and the reverse supply chains, the performance metrics and evaluation techniques used in traditional supply chain cannot be extended to reverse supply chains.
With the above drawbacks, in this dissertation, we examine the variety of issues that are critical for the effective and efficient planning of closed-loop supply chains.
The growing amount of waste created by products reaching the end of their useful life poses challenges for the environment, for governments, and for manufacturers. Alternatives for processing this waste include reuse, remanufacturing, recycling, storage, and disposal. With disposal considered to be the least desirable, the first process required by the remaining alternatives is disassembly. However, disassembly is labor intensive and therefore costly. Ensuring that the disassembly process is as efficient as possible is essential to enable economic justification of an end-of-life option other than disposal. Just as the assembly line is considered the most efficient way to assemble a product, the disassembly line is the most efficient way to disassemble a product. For this reason, efficient techniques are required to solve problems involving the number of workstations required and the disassembly sequencing of end-of-life products with application to the disassembly line. The challenge lies in the fact that this problem tends to have high calculation complexities due to its rapid growth in computational runtime with incremental increases in the number of parts. In addition, the added complications of disassembly typically foster multiple objectives including environmental and economical goals that can frequently be contradictory.
The multi-objective DISASSEMBLY LINE BALANCING PROBLEM seeks to find a disassembly solution sequence which: provides a feasible disassembly sequence, minimizes the number of workstations, minimizes total idle time, and ensures similar idle times at each workstation as well as addressing other, disassembly-specific concerns. Finding the optimal line balance is computationally intensive due to exponential growth. With exhaustive search calculations quickly becoming prohibitively large, methodologies from the field of combinatorial optimization hold promise for providing solutions to the DISASSEMBLY LINE BALANCING PROBLEM. In this dissertation, the DISASSEMBLY LINE BALANCING PROBLEM is described then defined mathematically and proven to belong to the class of unary NP-complete problems. Four instances of the problem are developed, then disassembly line versions of depth-first exhaustive search, genetic algorithm and ant colony optimization metaheuristics, a greedy algorithm, and greedy/hill-climbing and greedy/2-optimal hybrid heuristics are presented and compared along with an uninformed general-purpose search heuristic. The problem instances are used in numerical studies performed to illustrate the implementations and allow for quantitative and qualitative analysis and comparison. Conclusions drawn include the consistent generation of optimal or near-optimal solutions, the ability to preserve precedence, the speed of the techniques, and their practicality for implementation.
The focus of this research is on disassembly and is limited to the problem of balancing the paced disassembly line. Within this domain, seven different techniques are employed to solve four instances. The various techniques presented in this research form a body of knowledge directed at addressing problems involving disassembly lines.
Since the usage of packaging is growing in leaps and bounds in our society, the need to use packaging effectively and efficiently has gained attention. There is a pressing need to reduce the volume of packaging and resulting toxicity when disposed of.
Industries use transport packaging such as pallets, crates and metal-shipping containers for transferring large volumes of their goods from one place to another. “Returnable” system for transport packaging is expected to save costs and bring environmental benefits. Though transport packaging is claimed to be costly to purchase and maintain, the repeated use of the same packaging can compensate for extra investment.
The present work focuses on two issues that are important for successful implementation of transport packaging systems: (1) how can one design transport packaging that is economical and environmentally benign? and (2) how can one manage transport packaging through multiple uses? In this work, a diverse set of tools, techniques, and methodologies are employed to address these issues.
To cope with the first issue, this work uses an artificial neural network (ANN) approach to assess design trends for transport packaging in the conceptual design stage. The neural network is constructed and trained on defined design attributes to detect hidden relationships between select design attributes and life-cycle design performances. Once the network is trained, one can use the trained network as the prediction model for prospective design for transport packaging. Then, an approach that combines fuzzy set theory and genetic algorithm (GA) is proposed to the packaging material selection problem. In this problem, the fuzzy set theory is used for transforming select linguistic variables into numerical values that will be used in the GA optimization for the optimal solutions. Finally, a multi-objective integer programming (MOIP) and analytic hierarchy process (AHP) is employed for solving the trade-offs between economic and environmental criteria to ensure different needs of individual customer groups.
To address the second issue, this work focuses on strategic plans for dispatching transport packaging, delivering transport packaging to the target locations, and traceability systems for retrieving transport packaging for future use. In this work, a simulation approach is used for studying dispatching strategy for returnable transport packaging. Then, the analysis of process capability indices is modified to solve the problem of delivering services to customers within limited time windows. Finally, this work offers insights for using traceability systems such as barcode and radio frequency identification (RFID) systems for the retrieval management of transport packaging. An approach based on multiattribute utility function (MAUF) is presented for selecting an appropriate traceability system for efficient packaging retrieval management.
This work differentiates itself from others as follows. Most existing literature addresses only packaging characteristics and performances. This work focuses on economical and environmental issues of transport packaging. It looks at the design of transport packaging from the conceptual stage to the selection of materials to the end-of-life of transport packaging. In addition, this work also examines the important strategies for implementing transport packaging by considering dispatching strategies and delivery services through the use of traceability technologies so that the same transport packaging can be used multiple times. Through various techniques and methodologies applied to various case studies presented in this work, a body of knowledge has been generated that should help packaging designers and managers in successful implementation of transport packaging to fulfill the economical and environmentally benign needs.
While a forward supply chain consists of a series of activities required to produce new products from raw material and distribute the former to consumers, a reverse supply chain consists of a series of activities required to collect used products from consumers and re-process them (used products) to either recover their left-over market values or dispose them of. The combination of forward and reverse supply chains is called a closed-loop supply chain. In the past decade, there has been an explosive growth of reverse and closed-loop supply chains, both in scope and scale.
Strategic planning primarily involves the design (what products should be processed/produced in what facilities) of a supply chain. It is long-range planning and is typically performed every few years when a supply chain needs to expand its capabilities.
A few location models have been proposed in the literature for strategic planning (designing) of reverse and closed-loop supply chains. In the case of discrete location models, all the recovery facilities are assumed to potential (efficient) and in the case of continuous location models, it is assumed that potential recovery facilities were already established or can be established at the locations solved for. Also, each of these location models deals with a used product that is given to be economical (profitable). Evidently, though every location model realizes the importance of re-processing only an economical used product in potential recovery facilities, it does not show how to either select that used product from a set of different used products or identify those potential recovery facilities in a region where the supply chain is to be designed. Also, although many quantitative models have been reported in the literature for designing a forward supply chain, none of them address the issue of environmental consciousness.
With the above drawbacks (in the literature for designing forward, reverse, and closed-loop supply chains) as a starting point, in this dissertation, we examine a variety of issues that are crucial for a reverse or closed-loop supply chain to operate successfully.
This dissertation can be divided into two parts. The first part proposes approaches for crucial issues in strategic planning of a reverse supply chain, and the second part proposes approaches for crucial issues in strategic planning of a closed-loop supply chain (most of the issues in strategic planning of a reverse supply chain are valid in strategic planning of a closed-loop supply chain as well). Different approaches are proposed for some of the issues considered in different decision-making situations.
In the first part, we propose two approaches (one uses linear integer programming and the other uses physical programming) to solve the issue of selection of economical used products for re-processing in a reverse supply chain. Then, we propose two approaches (one uses eigen vector method and TOPSIS, and the other uses neural networks and TOPSIS) to solve the issue of evaluation of success potentials of collection centers that are being considered for inclusion in a reverse supply chain. Then, we propose four approaches (the first one uses Analytic Hierarchy Process, the second one uses physical programming, the third one uses eigen vector method and TOPSIS, and the fourth one uses neural networks and TOPSIS) to solve the issue of identification of potential recovery facilities in a region where a reverse supply chain is to be designed. Then, we propose two approaches (one uses linear integer programming and the other uses physical programming) to solve the issue of transportation of the right mix and quantities of products (used and re-processed) across a reverse supply chain. Then, we propose a fuzzy TOPSIS approach for evaluating the marketing strategy of a reverse supply chain. Then, we build an expert system using Bayesian updating and fuzzy set theory, which can help make a decision regarding the futurity (disassembly or repair) of a used product of interest. We also propose an approach that uses fuzzy quality function deployment and the method of total preferences to select potential markets from a set of candidate second-hand markets.
In the second part, we formulate a fuzzy cost-benefit function that can be used to perform a multi-criteria economic analysis to select economical new products to process (produce) in a closed-loop supply chain. Then, we propose a fuzzy TOPSIS approach to evaluate production facilities in a region where a closed-loop supply chain is to be designed.
The implementation of extended manufacturer responsibility, together with the new more rigid environmental legislation and public awareness, have caused a growing number of manufacturers to begin recycling and remanufacturing their used products (cores) after they are discarded by the consumers. In addition, the economic attractiveness of reusing products, subassemblies or parts, instead of disposing them, has further fueled this phenomenon. Remanufacturing is an industrial process in which worn-out products are restored to "like-new" conditions. Thus, remanufacturing provides quality standards of new products with used parts. Remanufacturing is not only a direct and preferable way to reduce the amount of waste generated, it also reduces the consumption of raw materials and resources. In this dissertation we address the remanufacturing and disposal strategies in hybrid systems where manufacturing and remanufacturing processes occur together. This dissertation can be divided into three main parts. The first part deals with modeling the remanufacturing system using an open queueing network (OQN) with finite buffers and examining the system performance measures under various ranges of system parameters (core return rate, recovery rate of returned products, demand rate, machine breakdown and repair rates). The second part determines the optimal remanufacturing and disposal levels to operate the remanufacturing system economically. Finally, the third part proposes buffer allocation algorithms for remanufacturing system with service time interruptions in one of three ways, viz. machine breakdown, server vacation or N-policy.
In the first part we introduce an open queueing network with finite buffers to model a remanufacturing system. The system consists of three modules, viz., a testing module for returned products, a disposition module for non-reusable returns and a remanufacturing module. The network is analyzed using the decomposition principle and the expansion methodology. We examine the trade-off between production capacity and buffering against uncertainty in the remanufacturing systems. We investigate the case of a single module remanufacturable item over different stages of the product's life-cycle on such performance measures as expected total cost, expected WIP inventory, throughput rate, and expected processing time.
In the second part we develop a comprehensive procedure that determines the optimal input quantities at each stage of the remanufacturing operation in which recovery rates at each stage of the process are stochastic. Each station in the system is subject to breakdown and has a finite buffer capacity. Repair times, breakdown times and service times follow exponential distributions. Optimization is done on the system’s expected total cost using a dynamic programming (DP) algorithm.
Finally, in the third part, first we develop a near optimal buffer allocation plan (NOBAP) specifically developed for a cellular remanufacturing system with finite buffers and unreliable servers. The buffer allocation algorithm distributes a given number of available buffer slots among the remanufacturing system stations to optimize the system’s performance. Then, we extend the buffer allocation algorithm for other service time interruptions, viz. server vacation and N-policy. The approach taken in this dissertation avoids having to evaluate all possible buffer allocations by utilizing a controlled search in conjunction with expansion methodology.
The growing amount of waste created by the end-of-life (EOL) electronic products has become a severe problem for the environment. One of the most efficient ways to minimize further environmental deterioration is to properly process the end-of-life products. Potential alternatives of EOL processing include reuse, remanufacturing, recycling, storage and disposal. Among these alternatives, disposal is considered to be least desirable. Disassembly is often the first process to start preparing a product for EOL processing. However, disassembly is labor intensive and hence tends to be expensive. Thus, minimizing the disassembly time is crucial in keeping the EOL processing within economic bounds. For this reason, efficient techniques are required to solve problems involving disassembly. The difficulty is that, these problems tend to be either NP-complete or have high calculation complexities. In addition, these typically foster multiple goals including environmental and economical goals that can frequently be contradictory.
The focus of this research is on disassembly and is limited to disassembly planning (how much to disassemble) and disassembly sequencing (how to disassemble). Within this domain, five different techniques are employed to solve five problems.
The first technique used is Integer Goal Programming in the context of disassembly planning. Here, a disassembly-to-order system is modeled to determine the best combination of multiple products to selectively disassemble EOL products to meet the demand for items and materials with a variety of physical, financial and environmental constraints and goals. The second technique used is Fuzzy Goal Programming to solve the disassembly planning problem. This is an extension of the previous model that introduces vagueness into the model. The third technique used is Linear Physical Programming, which is also an extension of the first technique that automatically determines the weights for user-defined targets for disassembly planning. As a result, the demands for items and materials are fulfilled while various physical, financial and environmental constraints as well as both tangible and intangible goals are considered. The fourth technique used is Multiple Objective Genetic Algorithm in the context of disassembly sequencing while preserving the precedence constraints. The final technique used is the Multiple Objective Tabu Search in the context of both disassembly planning and disassembly sequencing. The technique delivers near optimal non-dominated alternative solutions while preserving the precedence relationships among the components.
The various techniques presented in this research form a body of knowledge that should help in solving problems with Product Recovery involving multiple objectives and various physical, financial and environmental constraints.
Within the past two decades initiatives towards preserving the environment have led the legislative bodies to pass new rules and regulations that force manufacturers to operate cleaner and with less disposal. These rules require the manufacturers to take responsibility of their products at the end of their useful lives, which opens up new sectors that deal with product recycling and remanufacturing in an organized fashion. While some manufacturers delegate their end of life problems to companies specializing in that field, others opt to take the matter into their own hands. The companies that restructure their operations to include remanufacturing of their used products are known as hybrid production companies. In this dissertation, we focus on the inventory control problems of hybrid production systems.
Unlike the ordinary production systems, hybrid systems use two different input sources, viz., raw materials and used products. This complicates the inventory mechanism of these systems. Since the input sources for remanufacturing and manufacturing systems are different, their outputs also tend to have some differences. In other words, a hybrid system produces various versions of the same product.
A characteristic of a hybrid company is its flexibility to fulfill a demand with new or remanufactured products, tagged with different prices. Since both product types are capable of performing the same functions, they can substitute each other. In this dissertation, we investigate the implications of product substitution in hybrid systems. We define a revenue function to capture the impact of substitution. We model the problem as a discrete time Markov decision process to identify the optimal substitution policy amongst a variety of substitution rules. We generalize several properties related to one-way and two-way substitution, including the proof of the existence of a substitution trajectory that divides the state space into "substitute" and "do not substitute" regions. We also present an approximate fluid control model that reinstates the properties found while providing a simpler tool to generate inventory control policies.
In the second phase of the dissertation, we look at markets where the demand is indifferent to the product version. Here the hybrid production system faces the challenge of supplying one type of demand using different production lines. To control the material flow of such systems we propose a single-stage make-to-stock pull control model. Since the system has two parallel production lines, the control mechanism is required to make a decision as to which line to trigger when a product is pulled to the production stage. We present two alternative routing mechanisms to address this situation.
Finally, we integrate the pull type hybrid production mechanism with the substitution control policies. To that end, we present an adaptive kanban control mechanism with a substitution policy as a generalization to the pull type hybrid inventory control policies.
Manufacturers of consumer products are now challenged to take responsibility for their products from cradle to grave. Correspondingly, end-of-life issues must be acknowledged and accommodated. Among the most sophisticated and important products to consider are vehicles and electronics. The EOL infrastructure for automobiles is mature and profitable. The disassembler and shredder are able to accommodate a wide variety of vehicles and materials, while still earning profits. The markets for secondary materials such as ferrous and nonferrous metals are well established and generate significant revenues for both the disassembler and shredder, offsetting the vehicle processing costs. However, the introduction of new materials, and changes in the markets for secondary materials may impact their profitability.
The EOL infrastructure for electronics is relatively new, and not currently profitable unless the recycler receives a processing fee. Markets for the secondary materials obtained in electronics recycling are not yet mature, and are very sensitive to over-supply, challenging the recycler's ability to obtain a predictable revenue stream. As a result, many products that could be processed are not, due to economic issues. Correspondingly, the value present in those discarded parts and materials will not be reclaimed.
Goal programming is an effective tool for evaluating the viability of an established and mature recycling infrastructure. Modifications of this approach may be employed to evaluate the viability of more variable and less mature infrastructures. Using these techniques and models of the automotive and electronics recycling industries, the importance of secondary markets to the viability of product recycling is explored. By examining the sensitivity of recycler profits to changes in revenues obtained from the sale of the various parts and materials, and by evaluating the effect of increased markets for parts, the importance of identifying and fostering markets for secondary materials is highlighted.
In the manufacturing of printed circuit boards (PCBs), computer-controlled hoists for material-handling are commonly used in automated PCB production systems. For many such production systems, there are no storage buffers between successive production stages and the processing time for each stage is (upper and lower) bounded. In addition, because of the detrimental effect of oxidization on the quality of the product, the parts must be transported with "no-wait" between successive processing tanks. However, the sequence of moves of the hoist need not be the same as the move sequence of the parts, and by optimizing the schedule of the hoists, production throughput can be maximized.
Scheduling the movement of programmable hoist for production lines is generally known as the Hoist Scheduling Problem (HSP). HSP has been proven to be NP-complete, even for problems with single-hoist and mono-product configuration. There are two main approaches to this problem. The first is a deterministic approach where, given bounds on the processing times for each stage in the production process, a minimum cyclic schedule is sought. The second is a real-time approach where decisions are made "on-line" as to which tasks the hoists should perform next.
After an extensive literature review on HSP, a constraint-satisfaction approach for the cyclic HSP (CHSP) for lines with single-hoist, mono-product configuration is presented. A binary search procedure is proposed and a tighter bound to the cycle length is introduced to reduce the computation effort.
Compared with single-hoist problem, multi-hoist scheduling problem (MHSP) is significantly more difficult due to the additional constraint of hoist collision avoidance. Most multi-hoist schedules in industry are experimented on-line, which are costly and time consuming. The use of simulation will save both cost and time, and improve current manual approaches so that better schedules can be identified. To this end, we first develop simulation models based on data from several actual PCB electroplating lines to assess the performance of various hoist scheduling heuristics. Experimentation was conducted with different combination of hoist speed and inter-hoist distance to examine their effects on productivity. Next, a solution approach is given to solve MHSP in the presence of hoist or tank failures. Finally, we expand the study of MHSP to multi-product case. The objective is to investigate additional productivity gain of processing multi-product on the PCB production line simultaneously and identify good hoist scheduling heuristic that maximizes production throughput for all products.
There has been an increasing trend toward environmentally conscious manufacturing and product recovery (ECMPRO) among manufacturers around the world; this trend has developed in response to both regulatory (government) and consumer pressures. ECMPRO involves the entire life cycle of products, from conceptual design to final delivery, and ultimately to the end-of-life (EOL) disposal of the products, such that the environmental standards and requirements are satisfied. A major element of EOL processing is product recovery which includes recycling and remanufacturing. Both recycling and remanufacturing involve product disassembly in order to retrieve the desired parts and/or subassemblies. The vast majority of current disassembly activities are manual, entailing high labor costs, particularly in the presence of hazardous materials. In order to reduce the cost of disassembly process in the overall scheme of product recovery, it must be planned and executed efficiently.
Goals related to the disassembly of EOL products include determining the disassembly sequences and improving the systems where the process of disassembly is carried out. Identifying the important issues in disassembly, modeling their effects on disassembly and ultimately on product recovery and providing solution approaches that are capable of improving the existing disassembly systems are the primary objectives of this work.
First, an extensive literature review on ECMPRO is provided to develop a background on the environmental degradation and the development of environmentally friendly practices. Next, an algorithm is presented to identify a geometrically-based disassembly precedence matrix (DPM) from a CAD drawing of the product. The DPM is necessary to automatically generate disassembly sequence plans (DSPs) for product recycling and remanufacturing. Then, a modified branch and bound algorithm to automatically generate a (near) optimal DSP is presented. Since the generation of feasible DSPs is NP-complete, the algorithm uses a branch and bound mechanism to overcome the computational burden due to otherwise exhaustive nature of the disassembly sequencing problem.
Next, a new problem-which has never been addressed in the literature-, namely, the disassembly line balancing problem (DLBP), is introduced. Its objective is to improve the flow of parts in the disassembly system in a way such that both the utilization of the disassembly line is maximized and the demand for the parts retrieved from the returned products is met. A classification of important considerations related to the DLBP is given. A heuristic is presented to solve the DLBP under several assumptions. The heuristic is based on a priority function which is instrumental in identifying the "best" task to assign to a particular workstation. Later, a solution approach is given to solve the DLBP in the presence of task failures. Examples are presented to illustrate the algorithmic solution methods proposed for DSP generation and DLBP solution.
Interest in the area of disassembly and remanufacturing in the electronics industry has intensified in recent years. The abundance of used products scrapped has triggered a demand for reusable electronic components, priced at a fraction of the new components. As a result, manufacturers have started to realize that they must turn their attention to the development of new methodologies for reverse logistics. This dissertation addresses this need.
The reverse logistics problem encompasses many different characteristics of environmentally conscious manufacturing and planning, including disassembly, reuse, recycling, and remanufacturing. Reverse logistics is gaining increased attention not only because of environmental factors but also for economic reasons.
In this research, five main techniques are employed to solve five different problems.
The first technique addresses the need for designing products for disassembly. Product design is the dominant factor that influences the ease by which the product can be taken apart at the end of its life. All efforts involved in reverse logistics would be futile without initially designing products for disassembly. The technique involves solving the problem in one of two ways. The first way enumerates the different combinations for retrieving the components based on an objective function consisting of four variables in order to find the largest value of the Design for Disassembly Index (DfDI). The second way employs an Integer Programming (IP) approach to find the optimal DfDI.
The second technique develops a heuristic scheduling methodology to solve the sequencing of a two-stage disassembly/retrieval process. A scheduling heuristic is used to optimize the processing time of the overall process with respect to the product structure. The uniqueness of this approach is that it allows a process planner to breakdown the product's bill-of-materials into modules, each consisting of functional component groups. The heuristic first optimizes the processing time of each stage (resulting in an optimal sub-sequence) and later combines the sub-sequences to make a complete optimal disassembly/retrieval process plan.
The third technique uses Case-Based Reasoning (CBR) to quickly generate disassembly process plans in order to prevent interruptions during disassembly operations. CBR is a technique which allows a process planner to rapidly retrieve, reuse, revise, and retain solutions to past disassembly problems. As a result, CBR allows uninterrupted disassembly of a variety of products in a multiple-product/ multiple-manufacturer environment.
The fourth technique addresses the lot-sizing problem for product disassembly. For disassembly, reverse logistics usually consists of a mixed batch of products. Since different products yield different combinations of components, finding an optimal lot-size of products to disassemble is often desired. In this dissertation, this problem is solved using Integer Programming. The final technique optimizes a bi-directional supply chain system using a sophisticated reverse flow (from the end of lease, end-of-life, and/or returned products) for product remanufacturing. With a forecasted demand for certain remanufactured items within a planning horizon, the technique uses a modification of the Materials Requirements Planning (MRP) approach to find the number of components to retrieve in different periods for remanufacturing. After remanufacturing, the "like-new" products are reintroduced back to consumers.
In sum, the above-mentioned innovative approaches to problems associated with reverse logistics, lead to optimum use of resources without compromising the quality of our environment.
The objective of this research is to develop Petri net (PN) models and modeling techniques, which provide both quantitative and qualitative modeling capabilities and which can be applied to systems of arbitrary size. This is achieved in two emerging and increasingly important areas in manufacturing: kanban production systems and disassembly process planning.
In the first phase of this research, we develop stochastic, colored PN (SCPN) models of single- and multi-product just-in-time (JIT) production systems. The models are shown to be live and bounded for lines of arbitrary size (length). They are then simulated to determine system performance under different kanban control policies. We examine two kanban control policies, the traditional kanban system (TKS) and flexible kanban system (FKS) policies. TKS is designed for systems operating in stable environments with little or no variation. FKS is designed for systems operating in the presence of variation. We show that FKS is responsive to system variations and achieves the goals of the kanban philosophy, namely, delivering the right product at the right time in the right quantity while minimizing WIP and completed product inventory. We show this to be the case for both single- and multi-product production lines. The specific contributions of the first phase of this research include PN models of two-card kanban systems, multi-product kanban systems, and FKS, SCPN models of kanban systems, and application of FKS to multi-product kanban systems.
In the second phase of this research, we develop algorithms to automatically generate a disassembly PN (DPN) from a geometrically based disassembly precedence matrix. The first of these, the DPN-CAO algorithm, generates a DPN for products with complex AND/OR disassembly precedence relationships. The second, the DPN-XOR algorithm, generates a DPN for products with XOR disassembly precedence relationships. We show that the resulting DPNs are live, bounded, and reversible. The resulting DPNs can be analyzed to identify the optimal disassembly process plan (DPP) using the reachability tree method; however, this method is exhaustive. As a result, we develop a heuristic algorithm which uses product information (disassembly times, directions, and tools) and disassembly priorities (e.g., early removal of hazardous components) to generate an optimal or near-optimal DPP.
To our knowledge, these are the first algorithms to automatically generate DPNs for products with complex AND/OR and XOR precedence relationships. Further, we show that the resulting DPNs are live, bounded, and reversible. These algorithms can be used to handle products that are more complex than any that have appeared in the disassembly process planning literature, and have strong potential for industrial applications.
Surface Mount Technology (SMT) is a popular method of Printed Circuit Board (PCB) assembly in which high speed automated assembly machines are capable of placing in excess of 40,000 components per hour. In order to achieve these impressive assembly rates, complex placement machines must be programmed efficiently.
The complexity of a printed circuit board assembly process is highly dependent on the level of automation, type of placement or insertion system, and the characteristics of the board under production. In this thesis, an analysis of an automated surface mount process, with primary concentration on the High Speed Chip Shooter (HSCS) machine, is presented. Modeling the system, developing the problem formulation, and establishing alternative assembly methodologies that are capable of improving the existing surface mount process are the primary objectives of this work.
The HSCS machine consists of a feeder mechanism, pick and place mechanism, and a positioning table. One of the main planning difficulties with this type of system is establishing effective and efficient movement between the three concurrent mechanisms. Operations research principles and procedures are utilized in the development of the problem formulation and heuristic development with respect to the component placement sequence, the coordination of the mechanisms involved, and the minimization of the total system time per board.
Determining the component placement sequence, also referred to as the placement path, is an NP-complete problem that best resembles the Traveling Salesman Problem, while another problem posed by the coordination of the mechanisms closely resembles the Quadratic Assignment Problem. In this thesis, several heuristic algorithms are developed and tested against previously published subproblems, and applied to a real-life working board configuration.
Finally, the effect of electronics assembly, disassembly, and disposal on the environment is reviewed and the potential hazards of continuing the present trends in electronics parts disposal is discussed. This discussion emphasizes the growing size of this problem in a world of increasing technology, where electronic products dominate. In order to promote and support this new environmental ethic in electronics assembly and disassembly, the need for improved methods of electronics reuse, minimization of life-cycle scrap, development of planning tools, and an increase in research activity in this area is also highlighted.
Finite buffered queues are frequently used for modeling make-to-order production systems. Operational problems for the production system can then be studied as performance evaluation and optimization problems in the corresponding queueing model. A key operational issue for make-to-order manufacturing systems is dynamically scheduling production by selecting a processing rate when the system has different processing rates with different operating costs. If order arrivals are external to the system, production can be controlled by adjusting the processing rate. On the other hand, if order arrivals are internal, the production rate can be varied by controlling the input rate to the system. These concerns motivate an optimization problem in which an optimal operating policy has to be determined to balance the operating costs versus the holding costs and setup costs.
We consider a single server finite buffered queueing model of such a production system which may have external or internal order arrivals. In terms of the queueing framework, the production scheduling problems translate into problems of arrival and service control. There are two major contributions of this dissertation for this class of queueing problems. Firstly, a theoretical framework that relates input and service control problems is presented. It is shown that these problems can be treated in a unified manner by exploiting a queue length relationship called duality. The relationship between input and service control is especially of interest, if performance analysis and optimization methods for the systems in question are available. As a second contribution of the dissertation, we provide computational methods to analyze the effect of variances of the interarrival and service time distributions on the performance of the system.
After establishing a duality relationship for finite buffered queues with arrival and service control, different control models are considered in detail. A common challenge in all of these problems is evaluating the performance of the system under a specified operating policy. Efficient computational methods that are valid for a very general class of interarrival and service time distributions are presented. These methods enable us to compare different operating policies for a variety of interarrival and service time distributions with different cost and revenue structures. Extending the duality principle from a queue length relation to a system performance relation, new structural properties of the optimal operating policy for the arrival control can be obtained in some cases, thereby reducing the computational effort for finding the optimal policy.
It is frequently argued that the variances of the probability distributions that drive the system have a significant effect on the performance measures. However for most queueing systems, it is difficult to compute this effect exactly. As an important contribution, for the service and arrival control problems considered in this dissertation, this variability effect is quantified exactly. Numerical experimentation confirms the significance of this effect for both arrival and service controlled systems. Based on this finding, various approximation procedures that take the variability effect into account are presented when analysis methods are infeasible.
As manufacturing systems become more complex, their structures, modeling and analysis become equally complicated. One of the recent research interests in flexible manufacturing systems is in imposing finite buffer capacities in front of service stations. In many manufacturing systems however, machine availability is an important issue due to its impact on productivity. The objective of this research is two fold: 1) to integrate the concept of server unavailability to flexible manufacturing systems with finite buffer capacities and BAS (blocked after service) mechanism, and develop algorithms to calculate the throughput of tandem networks, and 2) to extend this work to arbitrary topology networks with split and merge configurations.
We consider three types of service interruptions viz., interruptions due to machine breakdowns, interruptions due to server vacations and interruptions due to the imposition of N-policy. We use open queueing networks to model the tandem and arbitrary topology networks. Due to difficulties in solving queueing networks with finite buffers in a closed form, we derive approximate expressions, using decomposition, isolation, and expansion concepts, for the performance measures of the various systems. Finally, various algorithms are developed by assembling these expressions into systematic step by step procedures.
In order to test the performances of the algorithms, we build several simulation models. We test algorithm results rigorously by comparing them to the simulation results. We test numerous networks. For each network, we impose different experimental conditions by varying the network parameters between a wide range of values. We use Taguchi's "robust design" methodology in order to keep the number of the experiments manageable, yet covering the entire experimental region. The results of the experiments show that the algorithms are robust and remarkably accurate over a wide range of parameters.
Disassembly and materials recovery is currently a growing trend in industrialized nations. It is a natural result of today's fundamental changes in economics, the alarming depletion of natural resources, and the powerful environmental movements. A few governments have already passed regulations that force manufacturers to take back and dismantle their used products so that components and materials can be reused and/or recycled. In addition to this new trend of disassembly, the manufacturing sector also has been experiencing other important trends such as components and materials commonality. All these trends and recent changes have led to this effort.
We principally address the operational and planning aspects of the material requirements for disassembly operations. Materials Requirements Planning (MRP) is a widely used procedure for production planning, but it is assembly oriented and cannot be used for the planning of disassembly operations. Perhaps the single most important difference between assembly and disassembly settings is the number of demand sources. In an assembly setting, the parts tend to converge to a single demand source and they are moving on the manufacturing floor. This single demand source is the final product, and the material management's governing principles are constrained by this "convergence" property. Under a disassembly setting, as the parts start moving away from their source of origin, they tend to diverge from each other and lead to a "divergence" property.
This thesis can be divided into two parts. The objective of the first part is to identify and analyze technical and operational challenges facing disassembly and their transitional and long-term impact on the manufacturing sector. The second part lays the foundation for a complete disassembly scheduling system of the future. To this end, three heuristic algorithms have been developed. These algorithms can be used to find a disassembly schedule for the root(s) and subassemblies when the demand occurs at the component level of the product structure. The three algorithms deal respectively with a single product structure with no commonality, a single product structure with commonalty, and multiple product structures with commonality. The problems that these algorithms address are non polynomial (NP), yet the complexity of the algorithms presented is of the order of O(n) where n is the number of root items. Thus, the algorithms are very efficient. The results obtained by using these algorithms were either optimal or very close to optimal.
This thesis focuses on Just-In-Time (JIT) systems. In particular, the production control aspects of JIT (i.e. the parts dealing with the Kanban System) are emphasized. JIT is designed for an ideal environment, e.g., smooth and stable demand, constant processing times, balanced stations and no equipment breakdowns. However, in most real life situations, this type of environment is rare, if not impossible, to achieve. It can be argued that the operational problems caused due to these imperfections can be minimized by judiciously manipulating the number of Kanbans in the manufacturing system. To this end, a newly developed system, referred to as the Flexible Kanban System (FKS), is introduced. The objective of FKS is to manipulate the number of Kanbans in order to minimize the detrimental effects of variations and uncertainties present in the manufacturing environment. However, the manipulation of the number of Kanbans requires a systematic approach which is sensitive to the type of problem being addressed.
The effect of FKS implementation has been examined in five manufacturing system problem areas (each of which is disastrous to the Traditional Kanban System (TKS)), viz., (1) stochastic processing time, (2) variable demand, (3) machine maintenance interruptions, (4) material handling system breakdown and (5) machine breakdown. In each case, a systematic algorithm has been developed for the FKS to cope with the particular problem being addressed and its performance has been compared to that of the TKS. Amongst the performance measures used to compare the two systems include, the average time in system, the average order completion time, the average inventory, the average number of units backlogged and resource utilization.
In an ideal environment (i.e., smooth and stable demand, constant processing times, balanced stations and no equipment breakdowns), FKS offers no additional advantage over TKS. In fact, its performance is identical to that of TKS. In almost all other cases considered, the performance of the FKS was found to be superior to that of the TKS.