A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines

Computers and Operations Research

Volumn 25, Issue 7-8, 1998, Pages 675-690

  Hyun, Chul Ju ^a,b   Kim, Yeongho ^c   Kim, Yeo Keun ^b  

^a Department of Quality Management   (South Korea)

^b Chonnam National University   (South Korea)

^c Seoul National University   (South Korea)

Author keywords

Genetic algorithm; Mixed model assembly lines; Multiple objectives; Sequencing problems

Indexed keywords

COST EFFECTIVENESS; DECISION THEORY; GENETIC ALGORITHMS; MATHEMATICAL MODELS; PARETO PRINCIPLE; PROBLEM SOLVING; PROCESS CONTROL; SCHEDULING; SUPERVISORY PERSONNEL;

MIXED MODEL ASSEMBLY LINES; SEQUENCING PROBLEMS;

PRODUCTION CONTROL;

EID: 0032120699     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0305-0548(98)00026-4     Document Type: Article

References (19)

1. Bard, J. F., Dar-El, E. M. and Shtub, A., An analytic framework for sequencing mixed model assembly lines. Int. J. Prod. Res., 1992, 30, 35-48.
2. Okamura, K. and Yamashina, H., A heuristic algorithm for the assembly line model-mix sequencing problem to minimize the risk of stopping the conveyor. Int. J. Prod. Res., 1979, 17, 233-247.
3. Dar-El, E. M., Mixed-model assembly line sequencing problems. Omega, 1978, 6, 313-323.
4. Yano, C. A. and Rachamadugu, R., Sequencing to minimize work overload in assembly lines with product options. Manage. Sci., 1991, 37, 572-586.
5. Burns, L. D. and Daganzo, C. F., Assembly line job sequencing principles. Int. J. Prod. Res., 1987, 25, 71-99.
6. Miltenburg, J., Level schedules for mixed-model assembly lines in just-in-time production systems. Manage. Sci., 1989, 35, 192-207.
7. Monden, Y., Toyota Production System, 2nd edn. Institute of Industrial Engineers, Norcross, Georgia, 1993.
8. Bard, J. F., Shtub, A. and Joshi, S. B., Sequencing mixed-model assembly lines to level parts usage and minimize line length. Int. J. Prod. Res., 1994, 32, 2431-2454.
9. Tsai, L., Mixed model sequencing to minimize utility work and the risk of conveyor stoppage. Manage. Sci., 1995, 41, 485-495.
10. Bolat, A., Savsar, M. and Al-Fawzan, M. A., Algorithms for real-time scheduling of jobs on mixed model assembly lines. Comput. Ops. Res., 1994, 21, 487-498.
11. Kim, Y. K., Hyun, C. J. and Kim, Y., Sequencing in mixed model assembly lines: a genetic algorithm approach. Comput. Ops. Res., 1996, 23, 1131-1145.
12. Holland, J. H., Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975.
13. Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin, 1992.
14. Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, 1989.
15. Schaffer, J. D., Multiple objective optimization with vector evaluated genetic algorithms. Proc. 1st Int. Conf. Genetic Algorithms and Their Applications. 1985, pp. 93-100.
16. Fonseca, C. M. and Fleming, P. J., Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. Proc. 5th Int. Conf. Genetic Algorithms. 1993, pp. 416-423.
17. Ritzel, B. J. and Eheart, J. W., Using genetic algorithms to solve a multiple objective groundwater pollution containment problem. Water Resource Res., 1994, 30, 1589-1603.
18. Baker, J. E., Adaptive selection methods for genetic algorithms. Proc. 1st Int. Conf. Genetic Algorithms and Their Applications. 1985, pp. 101-111.
19. Horn, J., Nafpliotis, N. and Goldberg, D. E., A niched Pareto genetic algorithm for multiobjective optimization. Proc. of 1st IEEE-ICEC Conference. 1994, pp. 82-87.