Industrial Engineering and Management Systems

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지
DOI QR코드

DOI QR Code

A Shaking Optimization Algorithm for Solving Job Shop Scheduling Problem

  • Abdelhafiez, Ehab A. (Mechanical and Industrial Engineering Department Faculty of Engineering, Majmaah University) ;
  • Alturki, Fahd A. (Electrical Engineering Department Faculty of Engineering, King Saud University)
  • Received : 2011.01.17
  • Accepted : 2011.02.21
  • Published : 2011.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

In solving the Job Shop Scheduling Problem, the best solution rarely is completely random; it follows one or more rules (heuristics). The Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing, and Tabu search, which belong to the Evolutionary Computations Algorithms (ECs), are not efficient enough in solving this problem as they neglect all conventional heuristics and hence they need to be hybridized with different heuristics. In this paper a new algorithm titled "Shaking Optimization Algorithm" is proposed that follows the common methodology of the Evolutionary Computations while utilizing different heuristics during the evolution process of the solution. The results show that the proposed algorithm outperforms the GA, PSO, SA, and TS algorithms, while being a good competitor to some other hybridized techniques in solving a selected number of benchmark Job Shop Scheduling problems.

Keywords

References

  1. Moraglio, A., Ten Eikelder, H. M. M., and Tadei, R. (2001), Genetic local search for job shop scheduling, Technical Report CSM-435 ISSN, 1744-8050.
  2. Jain, A. S. and Meeran, S. (1998), Job shop scheduling using neural networks, International Journal of Production Research, 36, 1249-1272. https://doi.org/10.1080/002075498193309
  3. Albert Jones, and Luis C. Rabelo (1998), Survey of job shop scheduling techniques, NISTIR, National Institute of Standards and Technology, Gaithers-burg, MD.
  4. Anthony Jack Carlisle (2002), Applying the particle swarm optimizer to non-stationary environments, A Dissertation Submitted to the Graduate Faculty of Auburn University in Partial Fulfillment of the Requirements of the Degree of Doctor of Philosophy, Auburn University.
  5. Chandrasekharan Rajendran, and Oliver Holthaus (1999), A comparative study of dispatching rules in dynamic flow shops and job shops, European Journal of Operational Research, 116, 156-170. https://doi.org/10.1016/S0377-2217(98)00023-X
  6. ChaoYong Zhang, PeiGen Li,YunQing Rao, and ZaiLin Guan (2008), A very fast TS/SA algorithm for the job shop scheduling problem, Computers and Operations Research, 35, 282-294. https://doi.org/10.1016/j.cor.2006.02.024
  7. Dirk C. Mattfeld a, and Christian Bierwirth (2004), An efficient genetic algorithm for job shop scheduling with tardiness objectives. European Journal of Operational Research, 155, 616-630. https://doi.org/10.1016/S0377-2217(03)00016-X
  8. Abdelhafiez, E. (2008), Efficient items-ordering rules in cutting stock problem, 9th International Conference on Mechanical Design and Production Engineering MDP9, Cairo-Egypt.
  9. Abdelhafiz, E., Elmaghraby, A. S., and Hassan, M. F. (2001), A genetic approach for 2-D irregular cutting stock problems, Proceedings of ISCA 10th international Conference on intelligent Systems, USA, 114-117.
  10. Jose Fernando Gonçalves, Jorge Jose de Magalhaes Mendes, and Maurício G. C. Resende (2005), A hybrid genetic algorithm for the job shop scheduling problem. European Journal of Operational Research, 167, 77-95. https://doi.org/10.1016/j.ejor.2004.03.012
  11. Steinhofel, K., Albrecht, A. and Wong, C. K. (1999), Two simulated annealing-based heuristics for the job shop scheduling problem, European Journal of Operational Research, 118, 524-548. https://doi.org/10.1016/S0377-2217(98)00326-9
  12. Liya GU, Alexandru Sava, Sophie Hennequin, and Xiaolan XIE (2007), Electromagnetism-like mechanism algorithm for stochastic assembly line balancing with reliability constant, Proceeding of International Conference on Industrial Engineering and System Management, IESM, Beijing-China.
  13. Emin Aydin, M. and Terence C. Fogarty (2004), A simulated annealing algorithm for multi-agent systems: a job shop scheduling application, Journal of Intelligent Manufacturing, 15.
  14. Fatih Tasgetiren, M., Mehmet Sevkli, Yun-Chia Liang, and Mutlu Yenisey, M. (2006), A particle swarm optimization and differential evolution algorithms for job shop scheduling problem, International Journal of Operations Research, 3, 120-135.
  15. Ventresca, M. and Ombuki, B. M. (2003), Meta-heuristics for the job shop scheduling problem, Technical Report # CS-03-12.
  16. Ventresca, M. and Ombuki, B. M. (2004), Ant Colony optimization for job shop scheduling problem, Technical Report # CS-04-04.
  17. Mehmet Sevkli, and Emin Aydin, M. (2006), Collaborating variable neighbourhood search algorithms for job shop scheduling problems, Proceedings of 5th International Symposium on Intelligent Manufacturing Systems, 450-461.
  18. Mehmet Sevkli, and Emin Aydin, M. (2006), Variable Neighbourhood Search for job shop scheduling problems, Journal Of Software, 1, 34-39.
  19. Mladenovic, N. and Hansen, P. (1997), Variable Neighborhood Search. Computers and Operations Research, 24, 1097-1100. https://doi.org/10.1016/S0305-0548(97)00031-2
  20. Senthil Vemurugan, P. and Selladurai, V. (2007), A Tabu Search Algorithm for job shop scheduling problem with industrial scheduling case study, International Journal Of Soft Computing, 2(4), 531-537.
  21. Peter Brucker (2007), The job-shop problem: old and new challenges. MISTA.
  22. Pisut Pongchairerks, and Voratas Kachitvichyanukul (2007), A comparison between algorithms VNS with PSO and VNS without PSO for job-shop scheduling problems. International Journal of Computational Science, 1, 179-191.
  23. Kamrul Hasan, S. M., Ruhul Sarker, Daryl Essam, and David Cornforth (2009), Memetic Algorithms for solving job-shop scheduling problems, Memetic Computing Journal, 1, 69-83. https://doi.org/10.1007/s12293-008-0004-5
  24. Vesterstrom, J. and Thomsen, R. (2004), A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems, Evolutionary Computation, CEC2004, Congress on, 2(19-23), 1980-1987.

Cited by

  1. Hybrid genetic algorithm for test bed scheduling problems vol.52, pp.4, 2011, https://doi.org/10.1080/00207543.2013.838327