JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Hybrid Genetic Algorithms for Solving Reentrant Flow-Shop Scheduling with Time Windows
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Hybrid Genetic Algorithms for Solving Reentrant Flow-Shop Scheduling with Time Windows
Chamnanlor, Chettha; Sethanan, Kanchana; Chien, Chen-Fu; Gen, Mitsuo;
  PDF(new window)
 Abstract
The semiconductor industry has grown rapidly, and subsequently production planning problems have raised many important research issues. The reentrant flow-shop (RFS) scheduling problem with time windows constraint for harddisk devices (HDD) manufacturing is one such problem of the expanded semiconductor industry. The RFS scheduling problem with the objective of minimizing the makespan of jobs is considered. Meeting this objective is directly related to maximizing the system throughput which is the most important of HDD industry requirements. Moreover, most manufacturing systems have to handle the quality of semiconductor material. The time windows constraint in the manufacturing system must then be considered. In this paper, we propose a hybrid genetic algorithm (HGA) for improving chromosomes/offspring by checking and repairing time window constraint and improving offspring by left-shift routines as a local search algorithm to solve effectively the RFS scheduling problem with time windows constraint. Numerical experiments on several problems show that the proposed HGA approach has higher search capability to improve quality of solutions.
 Keywords
Reentrant Flow-Shop;Time Windows;Hybrid Genetic Algorithm;Local Search Method;
 Language
English
 Cited by
1.
Embedding ant system in genetic algorithm for re-entrant hybrid flow shop scheduling problems with time window constraints, Journal of Intelligent Manufacturing, 2017, 28, 8, 1915  crossref(new windwow)
2.
Priority scheduling to minimize the total tardiness for remanufacturing systems with flow-shop-type reprocessing lines, The International Journal of Advanced Manufacturing Technology, 2017, 91, 9-12, 3697  crossref(new windwow)
 References
1.
Abe, K. and Ida, K. (2008), Genetic local search method for re-entrant flow shop problem, Proceedings of the Artificial Neural Networks in Engineering Conference, St. Louis, MO, 381-387.

2.
Chamnanlor, C. and Sethanan, K. (2009), Mixed integer programming models for scheduling hybrid flowshop with unrelated machines and time windows constraints, Proceedings of the 10th Asia Pacific Industrial Engineering and Management System Conference, Kitakyushu, Japan, 2331-2335.

3.
Chen, J. S., Pan, J. C. H., and Lin, C. M. (2008a), A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem, Expert Systems with Applications, 34(1), 570-577. crossref(new window)

4.
Chen, J. S., Pan, J. C. H., and Wu, C. K. (2007), Minimizing makespan in reentrant flow-shops using hybrid tabu search, International Journal of Advanced Manufacturing Technology, 34(3/4), 353-361. crossref(new window)

5.
Chen, J. S., Pan, J. C. H., and Wu, C. K. (2008b), Hybrid tabu search for re-entrant permutation flowshop scheduling problem, Expert Systems with Applications, 34(3), 1924-1930. crossref(new window)

6.
Chien, C. F. and Chen, C. H. (2007), A novel timetabling algorithm for a furnace process for semiconductor fabrication with constrained waiting and frequency-based setups, OR Spectrum, 29(3), 391-419. crossref(new window)

7.
Gao, J., Gen, M., and Sun, L. (2006), Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm, Journal of Intelligent Manufacturing, 17(4), 493-507. crossref(new window)

8.
Gen, M. and Cheng, R. (2000), Genetic Algorithms and Engineering Optimization, John Wiley and Sons, New York, NY.

9.
Gen, M., Cheng, R., and Lin, L. (2008), Network Models and Optimization: Multiobjective Genetic Algorithm Approach, Springer, London.

10.
Gen, M., Gao, J., and Lin, L. (2009), Multistage-based genetic algorithm for flexible job-shop scheduling problem, In: Intelligent and Evolutionary Systems, Springer, Heidelberg, Germany, 183-196.

11.
Gen, M., Tsujimura, Y., and Kubota, E. (1994), Solving job-shop scheduling problems by genetic algorithm, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, TX, 1577-1582.

12.
Goncalves, J. F., de Magalhaes Mendes, J. J., and Resende, M. G. (2005), A hybrid genetic algorithm for the job shop scheduling problem, European Journal of Operational Research, 167(1), 77-95. crossref(new window)

13.
Hwang, H. and Sun, J. U. (1997), Production sequencing problem with reentrant work flows and sequence dependent setup times, Computers and Industrial Engineering, 33(3), 773-776. crossref(new window)

14.
Jing, C., Tang, G., and Qian, X. (2008), Heuristic algorithms for two machine re-entrant flow shop, Theoretical Computer Science, 400(1), 137-143. crossref(new window)

15.
Kang, Y. H., Kim, S. S., and Shin, H. J. (2007), A scheduling algorithm for the reentrant shop: an application in semiconductor manufacture, International Journal of Advanced Manufacturing Technology, 35(5/6), 566-574. crossref(new window)

16.
Lin, L. and Gen, M. (2009), Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation, Soft Computing, 13(2), 157-168. crossref(new window)

17.
Lin, L., Gen, M., and Wang, X. (2009), Integrated multistage logistics network design by using hybrid evolutionary algorithm, Computers and Industrial Engineering, 56(3), 854-873. crossref(new window)

18.
Lin, L., Hao, X. C., Gen, M., and Jo, J. B. (2012), Network modeling and evolutionary optimization for scheduling in manufacturing, Journal of Intelligent Manufacturing, 23(6), 2237-2253. crossref(new window)

19.
Monch, L., Fowler, J. W., Dauzere-Peres, S., Mason, S. J., and Rose, O. (2011), A survey of problems, solution techniques, and future challenges in scheduleing semiconductor manufacturing operations, Journal of Scheduling, 14(6), 583-599. crossref(new window)

20.
Pan, J. H. and Chen, J. S. (2003), Minimizing makespan in re-entrant permutation flow-shops, Journal of the Operational Research Society, 54(6), 642-653. crossref(new window)

21.
Park, Y., Kim, S., and Jun, C. H. (2000), Performance analysis of re-entrant flow shop with single-job and batch machines using mean value analysis, Production Planning and Control, 11(6), 537-546. crossref(new window)

22.
Ruiz, R., Maroto, C., and Alcaraz, J. (2005), Solving the flowshop scheduling problem with sequence dependent setup times using advanced metaheuristics, European Journal of Operational Research, 165(1), 34-54. crossref(new window)

23.
Sethanan, K. (2001), Scheduling flexible flowshops with sequence dependent setup times, Ph.D. dissertation, West Virginia University, Morgantown, WV.