Abstract
From ancient times to the modern day, the concept of assembly has naturally been changed a lot. The most important milestone in assembly is the invention of assembly lines (ALs). In 1913, Henry Ford completely changed the general concept of assembly by introducing ALs in automobile manufacturing for the first time. He was the first to introduce a moving belt in a factory, where the workers were able to build the famous model-T cars, one piece at a time instead of one car at a time. Since then, the AL concept revolutionized the way products were made while reducing the cost of production. Over the years, the design of efficient assembly lines received considerable attention from both companies and academicians. A well-known assembly design problem is assembly line balancing (ALB), which deals with the allocation of the tasks among workstations so that a given objective function is optimized.
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References
Scholl, A. (1999). Balancing and Sequencing of Assembly Lines. Physica-Verlag, Heidelberg.
Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168, 694-715.
Baybars, I. (1986). A Survey of Exact Algorithms for the Simple Assembly Line Balancing Problem. Management Science, 32, 909-932.
Kim, Y. K., Kim, Y. J., & Kim, Y. H. (1996). Genetic algorithms for assembly line balancing with various objectives. Computers & Industrial Engineering, 30(3), 397-409.
Helgeson, W. B., Salveson, M. E., & Smith, W. W. (1954). How to balance an assembly line, Technical Report, Carr Press, New Caraan, Conn.
Salveson, M. E. (1955). The assembly line balancing problem. Journal of Industrial Engineering, 6, 18-25.
Bowman, E. H. (1960). Assembly line balancing by linear programming. Operations Research, 8(3), 385-389.
Held, M., Karp, R. M., & Shareshian, R. (1963). Assembly line balancing-Dynamic programming with precedence constraints. Operations Research, 11, 442-459.
Jackson, J. R. (1956). A Computing Procedure for a Line Balancing Problem. Management Science, 2, 261-272.
Karp, R. M. (1972). Reducibility among combinatorial problems. In Miller R.E & Thatcher J.W. Editors: Complexity of Computer Applications, 85-104, New York: Plenum Press.
Dar-El, E. M. (1973). MALB-A heuristic technique for balancing large single-model assembly lines. AIIE Transactions, 5(4), 343-356.
Scholl, A. & Voss, S. (1996). Simple assembly line balancing-heuristic approaches. Journal of Heuristics, 2, 217-244.
Suresh, G., & Sahu, S. (1994). Stochastic assembly line balancing using simulated annealing. International Journal of Production Research, 32(8), 1801-1810.
Falkenauer, E., & Delchambre, A. (1992). A genetic algorithm for bin packing and line balancing. Proceedings of the 1992 IEEE International Conference on Robotics and Automation, Nice, France, 1189-1192.
Bautista, J., & Pereira, J. (2002). Ant algorithms for assembly line balancing, Lecture Notes in Computer Science, 2463, 65-75.
Talbot, F. B., Patterson, J. H., & Gehrlein, W. V. (1986). A comparative evaluation of heuristic line balancing techniques. Management Science, 32, 430-454.
Ghosh, S., & Gagnon, R. J. (1989). A comprehensive literature review and analysis of the design, balancing and scheduling of assembly systems. International Journal of Production Research, 27, 637-670.
Erel, E., & Sarin, S. C. (1998). A survey of the assembly line balancing procedures. Production Planning and Control, 9, 414-434.
Rekiek, B., Dolgui, A., Delchambre, A., & Bratcu, A. (2002). State of art of optimization methods for assembly line design. Annual Reviews in Control, 26, 163-174.
Scholl, A. & Becker, C. (2006). State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. European Journal of Operational Research, 168, 666-693.
Rekiek, B. & Delchambre, A. (2006). Assembly line design: The balancing of mixed-model hybrid assembly lines with genetic algorithms. Springer Series in Advanced Manufacturing, London.
Ozmehmet Tasan, S. & Tunali, S. (2007). A review of the current applications of genetic algorithms in assembly line balancing. Journal of Intelligent Manufacturing, DOI: 10.1007/s10845-007-0045-5.
Gen, M., & Cheng, R. (2000). Genetic Algorithms and Engineering Optimization, New York: John Wiley & Sons.
Leu, Y. Y., Matheson, L. A., & Rees, L. P. (1994). Assembly line balancing using genetic algorithms with heuristic generated initial populations and multiple criteria. Decision Sciences, 15, 581-606.
Anderson, E. J., & Ferris, M. C. (1994). Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem. ORSA Journal on Computing, 6, 161-173.
Rubinovitz, J., & Levitin, G. (1995). Genetic algorithm for assembly line balancing. International Journal of Production Economics, 41, 343-354.
Tsujimura, Y., Gen, M., & Kubota, E. (1995). Solving fuzzy assembly line balancing using genetic algorithms. Computers & Industrial Engineering, 29(1-4), 543-547.
Suresh, G., Vinod, V. V., & Sahu, S. (1996). A genetic algorithm for assembly line balancing. Production Planning and Control, 7(1), 38-46.
Falkenauer, E. (1997). A grouping genetic algorithm for line balancing with resource dependent task times. Proceedings of the Fourth International Conference on Neural Information Processing, New Zealand, 464-468.
Ajenblit, D. A., & Wainwright, R. L. (1998). Applying genetic algorithms to the U-shaped assembly line balancing problem. Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, USA, 96-101.
Chan, C. C. K., Hui, P. C. L., Yeung, K. W., & Ng, F. S. F. (1998). Handling the assembly line balancing problem in the clothing industry using a genetic algorithm. International Journal of Clothing Science and Technology, 10(1), 21-37.
Kim, Y. J., Kim, Y. K., & Cho, Y. (1998). A heuristic-based genetic algorithms for workload smoothing in assembly lines. Computers & Operations Research, 25(2), 99-111.
Rekiek, B., de Lit, P., Pellichero, F., Falkenauer, E., & Delchambre, A. (1999). Applying the equal piles problem to balance assembly lines. Proceedings of the ISATP 1999, Porto, Portugal, 399-404.
Bautista, J., Suarez, R., Mateo, M., & Companys, R. (2000). Local search heuristics for the assembly line balancing problem with incompatibilities between tasks. Proceedings of the 2000 IEEE International Conference on Robotics and Automation, San Francisco, CA, 2404-2409.
Kim, Y. K., Kim, Y., & Kim, Y. J. (2000). Two-sided assembly line balancing: a genetic algorithm approach. Production Planning and Control, 11(1), 44-53.
Ponnambalam, S. G., Aravindan, P., Naidu, G., & Mogileeswar, G. (2000). Multi-objective genetic algorithm for solving assembly line balancing problem. International Journal of Advanced Manufacturing Technology, 16(5), 341-352.
Sabuncuoglu, I., Erel, E., & Tanyer, M. (2000). Assembly line balancing using genetic algorithms. Journal of Intelligent Manufacturing, 11(3) 295-310.
Carnahan, B. J., Norman, B. A., & Redfern, M. S. (2001). Incorporating physical demand criteria into assembly line balancing. IIE Transactions, 33, 875-887.
Simaria, A. S., & Vilarinho, P. M. (2001a). A genetic algorithm approach for balancing mixed model assembly lines with parallel workstations. Proceedings of The 6th Annual International Conference on Industrial Engineering Theory, Applications and Practice, November 18-20, 2001, San Francisco, USA.
Chen, R. S., Lu, K. Y., & Yu, S. C. (2002). A hybrid genetic algorithm approach on multiobjective of assembly planning problem. Engineering Applications of Artificial Intelligence, 15, 447-457.
Goncalves, J. F., & De Almedia, J. R. (2002). A hybrid genetic algorithm for assembly line balancing. Journal of Heuristic, 8, 629-642.
Miltenburg, J. (2002). Balancing and sequencing mixed-model U-shaped production lines. International Journal of Flexible Manufacturing Systems, 14, 119-151.
Valente, S. A., Lopes, H. S., & Arruda, L. V. R. (2002). Genetic algorithms for the assembly line balancing problem: a real-world automotive application. In: Roy, R., Kppen, M., Ovaska, S., Fukuhashi, T., Hoffman, F. Soft Computing in Industry - Recent Applications. Berlin: Springer-Verlag, 319-328.
Brudaru, O., & Valmar, B. (2004). Genetic algorithm with embryonic chromosomes for assembly line balancing with fuzzy processing times. The 8th International Research/Expert Conference Trends in the Development of Machinery and Associated Technology, TMT 2004, Neum, Bosnia and Herzegovina.
Martinez, U., & Duff, W. S. (2004). Heuristic approaches to solve the U-shaped line balancing problem augmented by Genetic Algorithms. In the Proceedings of the 2004 Systems and Information Engineering Design Symposium, 287-293.
Simaria, A. S., & Vilarinho, P. M. (2004). A genetic algorithm based approach to mixed model assembly line balancing problem of type II. Computers and Industrial Engineering, 47, 391-407.
Stockton, D. J., Quinn, L., & Khalil, R. A. (2004a). Use of genetic algorithms in operations management Part 1: applications. Proceeding of the Institution of Mechanical Engineers-Part B: Journal of Engineering Manufacture, 218(3), 315-327.
Stockton, D. J., Quinn, L., & Khalil, R. A. (2004b). Use of genetic algorithms in operations management Part 2: results. Proceeding of the Institution of Mechanical Engineers-Part B: Journal of Engineering Manufacture, 218(3), 329-343.
Brown, E. C., & Sumichrast, R. T. (2005). Evaluating performance advantages of grouping genetic algorithms. Engineering Applications of Artificial Intelligence, 18, 1-12.
Levitin, G., Rubinovitz, J., & Shnits, B. (2006). A genetic algorithm for robotic assembly line balancing. European Journal of Operational Research, 168, 811-825.
Noorul Haq, A., Jayaprakash, J., & Rengarajan, K. (2006). A hybrid genetic algorithm approach to mixed-model assembly line balancing. International Journal of Advanced Manufacturing Technology, 28, 337-341.
Runarsson, T. P., & Jonsson, M.T. (1999). Genetic production systems for intelligent problem solving. Journal of Intelligent Manufacturing, 10, 181-186.
Hoffmann T. R. (1992). EUREKA A hybrid system for assembly line balancing, Management Science, 38, 39-47
Chen, Y. Q. (2007). Study on Multi-objective Assembly Line Balancing Problem by Hybrid Evolutionary Algorithm. Ms Thesis, Waseda University, Graduate School of Information, Production and Systems, Kitakyushu, Japan.
Baudin, M. (2002). Lean Assembly: The nuts and bolts of making assembly operations flow. Productivity, New York.
Wemmerlov, U., & Hyer, N. L. (1989). Cellular manufacturing in the U.S. industry: A survey of users, International Journal of Production Research, 27(9), 1511-1530.
Miltenburg, J., & Wijngaard, J. (1994). The U-line line balancing problem. Management Science, 40(10), 1378-1388.
Monden, Y. (1998). Toyota production system–An integrated approach to just-in-time, 3rd ed. Dordrecht: Kluwer.
Scholl, A. & Klein, R. (1999). Balancing assembly lines effectively-a computational comparison. European Journal of Operational Research, 114, 50-58.
Hwang, R. K., Katayama, H., & Gen, M. (2007). U-shaped Assembly Line Balancing Problem with Genetic Algorithm. International Journal of Production Research, DOI: 10.1080/00207540701247906.
Rubinovitz, J. & Bukchin, J. (1993). RALB-a heuristic algorithm for design and balancing of robotic assembly line. Annals of the CIRP, 42, 497-500.
Rubinovitz, J. & Bukchin, J. (1991). Design and balancing of robotic assembly lines. Proceedings of the 4th World Conference on Robotics Research, Pittsburgh, PA.
Bukchin, J. & Tzur, M. (2000). Design of flexible assembly line to minimize equipment cost. IIE Transactions, 32, 585-598.
Tsai, D. M. &. Yao, M. J (1993). A line-balanced-base capacity planning procedure for seriestype robotic assembly line. International Journal of Production Research, 31, 1901-1920.
Kim, H. & Park, S. (1995). Strong cutting plane algorithm for the robotic assembly line balancing. International Journal of Production Research, 33, 2311-2323.
Khouja, M., Booth, D. E., Suh, M., & Mahaney, Jr. J. K. (2000). Statistical procedures for task assignment and robot selection in assembly cells. International Journal of Computer Integrated manufacturing, 13, 95-106.
Nicosia, G., Paccarelli, D. & Pacifici, A. (2002). Optimally balancing assembly lines with different workstations. Discrete Applied Mathematics, 118, 99-113.
Krasnogor, N., & Smith, J. (2000). A memetic algorithm with self-adaptive local search: TSP as a case study. Proceedings of Genetic and Evolutionary Computation Conference, July 10-12, Las Vegas, NV, 987-994, 2000.
Moscato, P., & Norman, M. (1992). A memetic approach for the traveling salesman problem: implementation of a computational ecology for combinatorial optimization on messagepassing systems. Proceedings of the International Conference on Parallel Computing and Transputer Applications, Amsterdam.
Scholl, A. (1993). Data of Assembly Line Balancing Problems. Schriften zur Quantitativen Betriebswirtschaftslehre 16/93, Th Darmstadt.
Gao, J. (2007). A study on Hybrid Genetic Algorithms for Manufacturing Optimization. PhD Thesis, Xi’an Jiaotong University, Xi’an, China.
Lin, L., Gen, M. & Gao, J. (2008) Optimization and improvement in robot-based assembly line system by hybrid genetic algorithm, IEEJ Transactions on Electronics, Information and Systems, in reviewing.
Meyr, H. (2004). Supply chain planning in the German automotive industry. OR Spectrum, 26(4), 447-470.
Miltenburg, J., & Sinnamon, G. (1989). Scheduling mixed model multi-level just-in-time production systems. International Journal of Production Research, 27, 1487-1509.
Brans, J.P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research, 24, 228-238.
Falkenauer, E. (1991). A genetic algorithm for grouping. Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis, Granada, Spain.
Rekiek, B. (2000). Assembly Line Design (multiple objective grouping genetic algorithm and the balancing of mixed-model hybrid assembly line). PhD Thesis, Free University of Brussels, CAD/CAM Department, Brussels, Belgium.
Falkenauer, E. (1998). Genetic Algorithms for Grouping Problems. Wiley, New York.
Falkenauer, E. (1995). Solving equal piles with the Grouping Genetic Algorithm. In L. J. Eshelman (ed.), Proceedings of the Sixth International Conference on Genetic Algorithms, 492-497. Morgan Kaufmann Publ., San Francisco, CA.
Optiline. www.optimaldesign.com/OptiLine/OptiLine.htm.
Ozmehmet Tasan, S. (2007). Solving Simple and Mixed-Model Assembly Line Balancing Problems Using Hybrid Meta-Heuristic Approaches. PhD Thesis, Dokuz Eylul University, Graduate School of Natural and Applied Sciences, Izmir, Turkey.
Goldberg, D. E. (2002). Design of innovation: Lessons from and for competent genetic algorithms. Boston, MA: Kluwer Acadamic Publishers.
Haupt, R. L. & Haupt, S. E. (2004). Practical Genetic Algorithms. Second edition with CD, Wiley, New York, NY.
Chiang, W. C. (1998). The application of a tabu search metaheuristic to the assembly line balancing problem. Annals of Operations Research, 77, 209-227.
Glover, F., & Laguna, M. (1998). Tabu Search. Dordrecht: Kluwer Academic Publishers.
Ozmehmet Tasan, S. & Tunali, S. (2006). Improving the genetic algorithms performance in simple assembly line balancing. Lecture Notes in Computer Science, 3984, 78-87.
Ozmehmet Tasan, S. & Tunali, S. (2007). Hybrid meta-heuristic approaches for solving SALBP. Computers & Industrial Engineering, in reviewing.
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(2008). Assembly Line Balancing Models. In: Network Models and Optimization. Decision Engineering. Springer, London. https://doi.org/10.1007/978-1-84800-181-7_7
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DOI: https://doi.org/10.1007/978-1-84800-181-7_7
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