Abstract
This paper presents a fuzzy extension of the simple assembly line balancing problem of type 2 (SALBP-2) with fuzzy job processing times since uncertainty, variability, and imprecision are often occurred in real-world production systems. The jobs processing times are formulated by triangular fuzzy membership functions. The total fuzzy cost function is formulated as the weighted-sum of two bi-criteria fuzzy objectives: (a) Minimizing the fuzzy cycle time and the fuzzy smoothness index of the workload of the line. (b) Minimizing the fuzzy cycle time of the line and the fuzzy balance delay time of the workstations. A new multi-objective genetic algorithm is applied to solve the problem whose performance is studied and discussed over known test problems taken from the open literature.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Anderson E. J., Ferris M. C. (1994) Genetic algorithms for combinatorial optimization: the assembly line balancing problem. INFORMS Journal on Computing 6: 161–173
Bäck T. (1996) Evolutionary algorithms in theory and practice. Oxford University Press, New York, NY
Baudin M. (2002) Lean assembly: The nuts and bolts of making assembly operations flow, productivity. Productivity Press, New York
Baybars I. (1986) A survey of exact algorithms for the simple assembly line balancing problem. Management Science 32: 909–932
Baykasoglu A. (2006) Multi-rule multi-objective simulated annealing algorithm for straight and U type assembly line balancing problems. Journal of Intelligent Manufacturing 17: 217–232
Becker C., Scholl A. (2006) A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research 168(3): 694–715
Brudaru, O., & Valmar, B. (2004). Genetic Algorithm with embryonic chromosomes for assembly line balancing with fuzzy processing times. In 8th international research/expert conference trends in the development of machinery and associated technology, TMT 2004. Neum, Bosnia and Herzegovina.
Chiang W. C. (1998) The application of a tabu search metaheuristic to the assembly line balancing problem. Annals of Operations Research 77: 209–227
Erel E., Sarin S. (1998) A survey of the assembly line balancing procedures. Production Planning and Control 9(5): 414–434
Gen M., Cheng R. (2000) Genetic algorithms and engineering optimisation. Wiley-Interscience, New York, NY
Gen M., Tsujimura Y., Li Y. (1996) Fuzzy assembly line balancing using genetic algorithms. Computers and Industrial Engineering 31(3/4): 631–634
Glover F. (1989) Tabu-search-Part I. ORSA Journal Computing 1(3): 190–206
Glover F. (1990) Tabu-search-Part II. ORSA Journal Computing 2(1): 4–32
Goldberg D. E. (1989) Genetic algorithm in search, optimization and machine learning. Addison Wesley, Reading, Massachusetts
Heinrici A. et al (1994) A comparison between simulated annealing and tabu search with an example from the production planning. In: Dyckhoff H. (eds) Operations research proceedings 1993. Springer, Berlin, pp 498–503
Holland J. H. (1975) Adaption in natural and artificial systems. University of Michigan Press, Ann Arbor, MI
Kaufmann A., Gupta M. M. (1985) Introduction to fuzzy arithmetic. Van Nostrand Reinhold, New York
Kim Y. K., Kim Y. J., Kim Y. (1996) Genetic algorithms for assembly line balancing with various objectives. Computers and Industrial Engineering 30(3): 397–409
Kirkpatrick S., Gelatt C. D. Jr., Vecchi M. P. (1983) Optimization by simulated annealing. Science 220: 671–680
Michalewitz Z. (1996) Genetic algorithms + data structures = evolution programs (3rd ed.). Springer, Berlin
Murata T., Ishibuchi H., Tanaka H. (1996) Multi-objective genetic algorithms and its application to flowshop scheduling. Computers and Industrial Engineering 30(4): 957–968
Nearchou A. C. (2008) Multi-objective balancing of assembly lines by population heuristics. International Journal of Production Research 46(8): 2275–2297
Oman S., Cunningham P. (2001) Using case retrieval to seed genetic algorithms. International Journal of Computational Intelligence and Applications 1(1): 71–82
Ozcan U., Toklu B. (2009) A new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems. Journal of Intelligent Manufacturing 20: 123–136
Rekiek B., De Lit P., Pellichero F., L’Englise T., Fouda P., Falkenauer E. et al (2001) A multiple objective grouping genetic algorithm for assembly line design. Journal of Intelligent Manufacturing 12: 467–485
Sabuncuoglu I., Erel E., Tanyer M. (2000) Assembly line balancing using genetic algorithms. Journal of Intelligent Manufacturing 11: 295–310
Scholl A. (1999) Balancing and sequencing of assembly lines. Physica-Verlag, Heidelberg, Germany
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(3): 666–693
Scholl A., Voß S. (1996) Simple assembly line balancing—Heuristic approaches. J Heuristics 2: 217–244
Tasan S. O., Tunali S. (2008) A review of the current applications of genetic algorithms in assembly line balancing. Journal of Intelligent Manufacturing 19(1): 49–69
Tsujimura Y., Gen M., Kubota E. (1995) Solving fuzzy assembly-line balancing problem with genetic algorithms. Computers and Industrial Engineering 29(1–4): 543–547
Watanabe T., Hashimoto Y., Nishikawa I., Tokumaru H. (1995) Line balancing using a genetic evolution model. Control Engineering Practice 3: 60–76
Zhang, W., Gen, M. (2009). An efficient multiobjective genetic algorithm for mixed-model assembly line balancing problem considering demand ratio-based cycle time. Journal of Intelligent Manufacturing., (available on-line) doi:10.1007/s10845-009-0295-5.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zacharia, P.T., Nearchou, A.C. Multi-objective fuzzy assembly line balancing using genetic algorithms. J Intell Manuf 23, 615–627 (2012). https://doi.org/10.1007/s10845-010-0400-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-010-0400-9