Application of Adaptive Particle Swarm Optimization to Bi-level Job-Shop Scheduling Problem

- Journal title : Industrial Engineering and Management Systems
- Volume 13, Issue 1, 2014, pp.43-51
- Publisher : Korean Institute of Industrial Engineers
- DOI : 10.7232/iems.2014.13.1.043

Title & Authors

Application of Adaptive Particle Swarm Optimization to Bi-level Job-Shop Scheduling Problem

Kasemset, Chompoonoot;

Kasemset, Chompoonoot;

Abstract

This study presents an application of adaptive particle swarm optimization (APSO) to solving the bi-level job-shop scheduling problem (JSP). The test problem presented here is JSP (ten jobs and ten machines) with tribottleneck machines formulated as a bi-level formulation. APSO is used to solve the test problem and the result is compared with the result solved by basic PSO. The results of the test problem show that the results from APSO are significantly different when compared with the result from basic PSO in terms of the upper level objective value and the iteration number in which the best solution is first identified, but there is no significant difference in the lower objective value. These results confirmed that the quality of solutions from APSO is better than the basic PSO. Moreover, APSO can be used directly on a new problem instance without the exercise to select parameters.

Keywords

Adaptive Particle Swarm Optimization;Job-Shop Scheduling;Bi-level Programming;

Language

English

Cited by

References

1.

Ai, T. J. and Kachitvichyanukul, V. (2007), Dispersion and velocity indices for observing dynamic behavior of particle swarm optimization, Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, 3264-3271.

2.

Ai, T. J. and Kachitvichyanukul, V. (2008a), A study on adaptive particle swarm optimization for solving vehicle routing problems, Proceedings of the 9th Asia Pacific Industrial Engineering and Management Systems Conference, Bali, Indonesia.

3.

Ai, T. J. and Kachitvichyanukul, V. (2008b), Adaptive particle swarm optimization algorithms, Proceedings of the 4th International Conference on Intelligent Logistics Systems, Shanghai, China, 460-469.

4.

Arumugam, M. S. and Rao, M. V. C. (2008), On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems, Applied Soft Computing, 8(1), 324-336.

5.

Chander, A., Chatterjee, A., and Siarry, P. (2011), A new social and momentum component adaptive PSO algorithm for image segmentation, Expert Systems with Applications, 38(5), 4998-5004.

6.

Cheng, R., Gen, M., and Tsujimura, Y. (1996), A tutorial survey of job-shop scheduling problems using genetic algorithms: I. Representation, Computers and Industrial Engineering, 30(4), 983-997.

7.

Gao, Y. and Ren, Z. (2007), Adaptive particle swarm optimization algorithm with genetic mutation operation, Proceedings of the 3rd International Conference on Natural Computation, Haikou, China, 211-215.

8.

Kachitvichyanukul, V. (2012), Comparison of three evolutionary algorithms: GA, PSO, and DE, Industrial Engineering and Management Systems, 11(3), 215-223

9.

Kasemset, C. (2009), TOC based job-shop scheduling, dissertation, Asian Institute of Technology, Pathumthani, Thailand.

10.

Kasemset, C. and Kachitvichyanukul, V. (2007), Simulation- based procedure for bottleneck identification. In: AsiaSim 2007, Springer, Heidelberg, Germany, 47-55.

11.

Kasemset, C. and Kachitvichyanukul, V. (2010), Bi-level multi-objective mathematical model for job-shop scheduling: the application of Theory of Constraints, International Journal of Production Research, 48 (20), 6137-6154.

12.

Kasemset, C. and Kachitvichyanukul, V. (2012), A PSObased procedure for a bi-level multi-objective TOCbased job-shop scheduling problem, International Journal of Operational Research, 14(1), 50-69.

13.

Kennedy, J. and Eberhart, R. (1995), Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Network, Perth, WA, 1942-1948.

14.

Kimms A. (1999), A genetic algorithm for multi-level, multi-machine lot sizing and scheduling, Computers and Operations Research, 26(8), 829-848.

15.

Kuo, R. J. and Huang, C. C. (2009), Application of particle swarm optimization algorithm for solving bilevel linear programming problem, Computers and Mathematics with Applications, 58(4), 678-685.

16.

Kuo, R. J. and Han, Y. S. (2011), A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem: a case study on supply chain model, Applied Mathematical Modelling, 35(8), 6905-3917.

17.

Lei, D. (2008), A Pareto archive particle swarm optimization for multi-objective job shop scheduling, Computers and Industrial Engineering, 54(4), 960-971.

18.

Lian, Z., Jiao, B., and Gu, X. (2006), A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan, Applied Mathematics and Computation, 183(2), 1008-1017.

19.

Lin, F. R., Shaw, M. J., and Locascio, A. (1997), Scheduling printed circuit board production systems using the two-level scheduling approach, Journal of Manufacturing Systems, 16(2), 129-149.

20.

Logendran, R., Mai, L., and Talkington, D. (1995), Combined heuristics for bi-level group scheduling problems, International Journal of Production Economics, 38(2/3), 133-145.

21.

Pan, Q. K., Fatih Tasgetiren, M., and Liang, Y. C. (2008), A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem, Computers and Operations Research, 35(9), 2807-2839.

22.

Pezzella, F., Morganti, G., and Ciaschetti, G. (2008), A genetic algorithm for the flexible job-shop scheduling problem, Computers and Operations Research, 35(10), 3202-3212.

23.

Pongchairerks, P. and Kachitvichyanukul, V. (2009), A two-level particle swarm optimisation algorithm on job-shop scheduling problems, International Journal of Operational Research, 4(4), 390-411.

24.

Pratchayaborirak, T. and Kachitvichyanukul, V. (2011), A two-stage PSO algorithm for job shop scheduling problem, International Journal of Management Science and Engineering Management, 6(2), 83-92.

25.

Rahimi-Vahed, A. R. and Mirghorbani, S. M. (2007), A multi-objective particle swarm for a flow shop scheduling problem, Journal of Combinatorial Optimization, 13(1), 79-102.

26.

Semnani, S. H. and Zamanifar, K. (2010), New approach to multi-level processor scheduling, International Journal on Artificial Intelligence Tools, 19(3), 335-346.

27.

Sha, D. Y. and Hsu, C. Y. (2006), A hybrid particle swarm optimization for job shop scheduling problem, Computers and Industrial Engineering, 51(4), 791-808.

28.

Shi, Y. and Eberhart, R. (1998), A modified particle swarm optimizer, Proceedings of the IEEE International Conference on Evolutionary Computation, Anchorage, AK, 69-73.

29.

Ueno, G., Yasuda, K., and Iwasaki, N. (2005), Robust adaptive particle swarm optimization, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, HI, 3915-3920.

30.

Wisittipanich, W. and Kachitvichyanukul, V. (2013), An efficient PSO algorithm for finding Pareto-frontier in multi-objective job shop scheduling problems, Industrial Engineering and Management Systems, 12(2), 151-160.