A simulated annealing algorithm approach to hybrid flow shop scheduling with sequence-dependent setup times
- 547 Downloads
One of the scheduling problems with various applications in industries is hybrid flow shop. In hybrid flow shop, a series of n jobs are processed at a series of g workshops with several parallel machines in each workshop. To simplify the model construction in most research on hybrid flow shop scheduling problems, the setup times of operations have been ignored, combined with their corresponding processing times, or considered non sequence-dependent. However, in most real industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacturing, hybrid flow shop problems have sequence-dependent setup times (SDST). In this research, the problem of SDST hybrid flow shop scheduling with parallel identical machines to minimize the makespan is studied. A novel simulated annealing (NSA) algorithm is developed to produce a reasonable manufacturing schedule within an acceptable computational time. In this study, the proposed NSA uses a well combination of two moving operators for generating new solutions. The obtained results are compared with those computed by Random Key Genetic Algorithm (RKGA) and Immune Algorithm (IA) which are proposed previously. The results show that NSA outperforms both RKGA and IA.
KeywordsScheduling Hybrid flow shop Sequence-dependent setup times Makespan Meta-heuristic Simulated annealing
Unable to display preview. Download preview PDF.
- Aarts E. H. L., Korst J. (1989) Simulated annealing and boltzmann machines: A stochastic approach to combinatorial optimization and neural computing. Wiley, Chi Chester, EnglandGoogle Scholar
- Aarts E. H. L., Lenstra J. K. (1997) Local search in combinatorial optimization. Wiley, Chi Chester, EnglandGoogle Scholar
- Arthanary T. S., Ramaswamy K.G. (1971) An extension of two machine sequencing problems. Operations Research 8: 10–22Google Scholar
- Fleischer, M. A. (1995). Assessing the performance of the simulated annealing algorithm using information. Theory (Doctoral Dissertation), Department of Operations Research, Case Western Reserve University, Cleveland, Ohio.Google Scholar
- Fleischer, M. A. (1995). Simulated annealing: Past, present, and future. In: C. Alexopoulos, K. Kang, W. R. Lilegdon and D. Goldsman (eds.), Proceedings of the 1995 Winter Simulation Conference, IEEE Press, pp. 155–161.Google Scholar
- Glover F., Kochenberger G. A. (2003) Handbook of metaheuristics. Stanford University, StanfordGoogle Scholar
- Leon V. J., Ramamoorthy B. (1997) An adaptable problem-space-based search method for flexible flow line scheduling. IIE Transactions 29: 115–125Google Scholar
- Pinedo M. (1995) Scheduling theory, algorithms, and systems. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
- Pugazhendhi S., Thiagarajan S., Rajendran C., Anantharaman N. (2004) Generating non-permutation schedules in flow line based manufacturing systems with sequence-dependent setup times of jobs: a heuristic approach. International Journal of Advanced Manufacturing Technology 23: 64–78CrossRefGoogle Scholar
- Van Laarhoven P. J. M., Aarts E. H. L. (1987) Simulated annealing: Theory and applications. D. Reidel Kluwer, Academic Publisher, Dordrecht, Boston, Norwell, MassachusettsGoogle Scholar