A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

Computers and Industrial Engineering

Volumn 53, Issue 4, 2007, Pages 642-666

  Rahimi Vahed, Alireza ^a   Mirzaei, Ali Hossein ^b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b TARBIAT MODARES UNIVERSITY   (Iran)

Author keywords

Bacterial optimization; Mixed model assembly line; Multi objective genetic algorithm; Multi objective sequencing problem; Multi objective shuffled frog leaping algorithm

Indexed keywords

COMPUTATION THEORY; GENETIC ALGORITHMS; OPTIMIZATION; PROBLEM SOLVING; PRODUCTION CONTROL;

BACTERIAL OPTIMIZATION; MIXED-MODEL ASSEMBLY LINE; MULTI-OBJECTIVE GENETIC ALGORITHM; MULTI-OBJECTIVE SEQUENCING PROBLEM; MULTI-OBJECTIVE SHUFFLED FROG-LEAPING ALGORITHM;

APPROXIMATION ALGORITHMS;

EID: 35348847056     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cie.2007.06.007     Document Type: Article

References (40)

1. Bard J.F., Dar-El E.M., and Shtub A. An analytic framework for sequencing mixed model. International Journal of Production Research 30 (1992) 35-48
2. Bard J.F., Shtub A., and Joshi S.B. Sequencing mixed-model assembly lines to level parts usage and minimize the length. International Journal of Production Research 32 (1994) 2431-2454
3. Beausoleil R.P. "MOSS" multiobjective scatter search applied to non-linear multiple criteria optimization. European Journal of Operational Research 169 (2006) 426-449
4. Ben-Daya M., and Al-Fawzan M. A tabu search approach for the flow shop scheduling problem. European Journal of Operational Research 109 (1998) 88-95
5. Botzheim, J., Koczy, L. T. (2004). Model Identification by Bacterial Optimization, Research supported by the National Scientific Research Fund OTKA T034233 and T034212, a Szechenyi University Research Grant 2004, and the National Research and Development Project Grant 2/0015/2002.
6. Coello Coello C.A., and Toscano Pulido G. A micro-genetic algorithm for multiobjective optimization. In: Zitzler E., Thiele K.D.L., Coello Coello C.A., and Corne D. (Eds). First international conference on evolutionary multi-criterion optimization, lecture notes in computer sciences, No. 1993 (2001), Springer-Verlag 126-140
7. Collette Y., and Siarry P. Multiobjective optimization: Principles and case studies (2003), Springer
8. Dawkins R. The selfish gene (1976), Oxford University Press, Oxford
9. Deb K. Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation Journal 7 3 (1999) 205-230
10. Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6 2 (2002) 182-197
11. Elbeltagi E., Hegazy T., and Grierson D. Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics 19 (2005) 43-53
12. Eusuff M.M., and Lansey K.E. Optimizing of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management 129 3 (2003) 210-225
13. Eusuff M., Lansey K., and Pasha F. Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization. Engineering Optimization 38 2 (2006) 129-154
14. Fonseca C.M., and Fleming P.J. Genetic algorithms for multi-objective optimization: Formulation, discussion, and generalization. In: Forrest S. (Ed). Proceedings of the fifth international conference on genetic algorithms (1993), Morgan Kaufman Publishers, San Mateo, California, University of Illinois at Urbana-Champaign 416-423
15. Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multi-objective optimization. In Proceedings of the 1st IEEE-ICEC Conference (pp. 82-87).
16. Hyun C.J., Kim Y., and Kim Y.K. A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines. Computers & Operations Research 25 7-8 (1998) 675-690
17. Inman P.R., and Bulfin R.L. Note on sequencing JIT mixed-model assembly lines. Management Science 37 7 (1991) 904-910
18. Jaszkiewicz, A. (1999). Genetic local search for multiple objective combinatorial optimization, Technical Report RA-014/98, Institute of Computing Science, Poznan University of Technology: Technical Report.
19. Knowles J.D., and Corne D.W. The Pareto archieved evolution strategy: A new baseline algorithm for multiobjective optimization, in: Congress on Evolutionary Computation (1999), IEEE Service Center, Washington, DC pp. 98-105
20. Korkmazel T., and Meral S. Bicriteria sequencing methods for the mixed-model assembly line in Just-in-time Production systems. European Journal of Operational Research 131 (2001) 188-207
21. Lian, Z., Gu, X., & Jiao, B. (in press). A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan, CHAOS SOLITOND & FRACTALS 2006.
22. Mansouri S.A. A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly Lines. European Journal of Operational Research 167 3 (2005) 696-716
23. McMullen P.R. An JIT sequencing for mixed-model assembly lines with setups using tabu search production. Planning & Control 9 5 (1998) 504-510
24. McMullen P.R. An efficient frontier approach to addressing JIT sequencing problems with setups via search heuristics. Computers & Industrial Engineering 41 (2001) 335-353
25. McMullen P.R. A Kohonen self-organizing map approach to addressing a multiple objective, mixed-model JIT sequencing problem. International Journal of Production Economics 72 (2001) 59-71
26. McMullen P.R. An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives. Artificial Intelligence in Engineering 15 (2001) 309-317
27. McMullen P.R., and Frazier G.V. A simulated annealing approach to mixed-model sequencing with multiple objectives on a JIT line. IIE Transactions 32 8 (2000) 679-686
28. Miltenburg J. Level schedules for mixed-model assembly lines in just-in-time production systems. Management Science 35 2 (1989) 192-207
29. Miltenburg J., Steiner G., and Yeomans S. A dynamic programming algorithm for scheduling mixed-model just-in-time production systems. Mathematical Computation Modeling 13 (1990) 57-66
30. Monden Y. Toyota production system (1983), Institute if industrial engineers Press, Atlanta, GA
31. Okamura K., and Yamshina H. A heuristic algorithm for the assembly line model-mix sequencing problem to minimize the risk of stopping the conveyor. International Journal of Production Research 17 (1979) 233-247
32. Pilegaard Hansen, M. (1997). Tabu search in multiobjective optimization: MOTS. In Proceedings of the 13th International Conference on Multiple Criteria Decision Making (MCDM_97), Cape Town, South Africa.
33. Schaffer J.D. Multiple objective optimization with vector evaluated genetic algorithms. In: Schaffer J.D. (Ed). Genetic algorithms and their applications: Proceedings of the first international conference on genetic algorithms (1985), Lawrence Erlbaum, Hillsdale, NJ 93-100
34. Srinivas N., and Deb K. Multi-objective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation 2 2 (1994) 221-248
35. Tavakkoli-Moghaddam R., and Rahimi-Vahed A.R. Multi-criteria sequencing problem for a mixed-model assembly line in a JIT production system. Applied Mathematics and Computation 181 2 (2006) 1471-1481
36. Veldhuizen, D. V., & Lamont, G. (1999). In J. Carroll, H. Haddad, D. Oppenheim, B. Bryant, G. Lamont (Eds.), Multi-objective Evolutionary Algorithm Test Suites. (pp. 351-357).
37. Xu J., Chiu S.Y., and Glover F. Fine-tuning a tabu search algorithm with statistical tests. International Transactions in Operational Research 5 3 (1998) 233-244
38. Yano C.A., and Rachamadugu R. Sequencing to minimize work overload in assembly lines with product options. Management Science 37 (1991) 572-586
39. Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. In K. Giannakoglou, D. Tsahalis, J. Periaux, P. Papailou, T. Fogarty (Eds.), EUROGEN 2001, Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece, September, pp. 95-100.
40. Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength pareto evolutionary algorithm. Computer Engineering and Networks Laboratory (TIK)-Report 103.