IDEAS home Printed from https://ideas.repec.org/p/rug/rugwps/10-679.html
   My bibliography  Save this paper

A hybrid single and dual population search procedure for the job shop scheduling problem

Author

Listed:
  • V. SELS
  • K. CRAEYMEERSCH
  • M. VANHOUCKE

Abstract

This paper presents a genetic algorithm and a scatter search procedure to solve the well-known job shop scheduling problem. In contrast to the single population search performed by the genetic algorithm, the scatter search algorithm splits the population of solutions in a diverse and high-quality set to exchange information between individuals in a controlled way. Extensions from a single to a dual population by taking problem specific characteristics into account can be seen as a stimulator to add diversity in the search process, which has a positive influence on the important balance between intensification and diversification. Computational experiments verify the benefit of this diversity on the effectiveness of the meta-heuristic search process. Various algorithmic parameters from literature are embedded in both procedures and a detailed comparison is made. A set of standard instances is used to compare the different approaches and the best obtained results are benchmarked against heuristic solutions found in literature.

Suggested Citation

  • V. Sels & K. Craeymeersch & M. Vanhoucke, 2010. "A hybrid single and dual population search procedure for the job shop scheduling problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/679, Ghent University, Faculty of Economics and Business Administration.
    • Handle: RePEc:rug:rugwps:10/679
    as

    Download full text from publisher

    File URL: http://wps-feb.ugent.be/Papers/wp_10_679.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Blazewicz, Jacek & Domschke, Wolfgang & Pesch, Erwin, 1996. "The job shop scheduling problem: Conventional and new solution techniques," European Journal of Operational Research, Elsevier, vol. 93(1), pages 1-33, August.
    2. Robert H. Storer & S. David Wu & Renzo Vaccari, 1992. "New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling," Management Science, INFORMS, vol. 38(10), pages 1495-1509, October.
    3. De Giovanni, L. & Pezzella, F., 2010. "An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem," European Journal of Operational Research, Elsevier, vol. 200(2), pages 395-408, January.
    4. Varela, Ramiro & Vela, Camino R. & Puente, Jorge & Gomez, Alberto, 2003. "A knowledge-based evolutionary strategy for scheduling problems with bottlenecks," European Journal of Operational Research, Elsevier, vol. 145(1), pages 57-71, February.
    5. Vilcot, Geoffrey & Billaut, Jean-Charles, 2008. "A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 190(2), pages 398-411, October.
    6. Joseph Adams & Egon Balas & Daniel Zawack, 1988. "The Shifting Bottleneck Procedure for Job Shop Scheduling," Management Science, INFORMS, vol. 34(3), pages 391-401, March.
    7. Jain, A. S. & Meeran, S., 1999. "Deterministic job-shop scheduling: Past, present and future," European Journal of Operational Research, Elsevier, vol. 113(2), pages 390-434, March.
    8. David Applegate & William Cook, 1991. "A Computational Study of the Job-Shop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 3(2), pages 149-156, May.
    9. Marti, Rafael & Laguna, Manuel & Glover, Fred, 2006. "Principles of scatter search," European Journal of Operational Research, Elsevier, vol. 169(2), pages 359-372, March.
    10. J. Carlier & E. Pinson, 1989. "An Algorithm for Solving the Job-Shop Problem," Management Science, INFORMS, vol. 35(2), pages 164-176, February.
    11. Waiman Cheung & Hong Zhou, 2001. "Using Genetic Algorithms and Heuristics for Job Shop Scheduling with Sequence-Dependent Setup Times," Annals of Operations Research, Springer, vol. 107(1), pages 65-81, October.
    12. Egon Balas & Alkis Vazacopoulos, 1998. "Guided Local Search with Shifting Bottleneck for Job Shop Scheduling," Management Science, INFORMS, vol. 44(2), pages 262-275, February.
    13. Pezzella, Ferdinando & Merelli, Emanuela, 2000. "A tabu search method guided by shifting bottleneck for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 120(2), pages 297-310, January.
    14. Kolonko, M., 1999. "Some new results on simulated annealing applied to the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 113(1), pages 123-136, February.
    15. V. Sels & F. Steen & M. Vanhoucke, 2010. "A hybrid job shop procedure for a Belgian manufacturing company producing industrial wheels and castors in rubber," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/678, Ghent University, Faculty of Economics and Business Administration.
    16. Taillard, E., 1993. "Benchmarks for basic scheduling problems," European Journal of Operational Research, Elsevier, vol. 64(2), pages 278-285, January.
    17. Goncalves, Jose Fernando & de Magalhaes Mendes, Jorge Jose & Resende, Mauricio G. C., 2005. "A hybrid genetic algorithm for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 167(1), pages 77-95, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Rui & Song, Shiji & Wu, Cheng, 2013. "A hybrid artificial bee colony algorithm for the job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 141(1), pages 167-178.
    2. Zhang, Rui & Chang, Pei-Chann & Wu, Cheng, 2013. "A hybrid genetic algorithm for the job shop scheduling problem with practical considerations for manufacturing costs: Investigations motivated by vehicle production," International Journal of Production Economics, Elsevier, vol. 145(1), pages 38-52.
    3. González, Miguel A. & Vela, Camino R. & Varela, Ramiro, 2015. "Scatter search with path relinking for the flexible job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 245(1), pages 35-45.
    4. Chong Peng & Guanglin Wu & T Warren Liao & Hedong Wang, 2019. "Research on multi-agent genetic algorithm based on tabu search for the job shop scheduling problem," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-19, September.
    5. Habibeh Nazif, 2015. "Solving Job Shop Scheduling Problem Using an Ant Colony Algorithm," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 5(5), pages 261-268, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Susana Fernandes & Helena Ramalhinho-Lourenço, 2007. "A simple optimised search heuristic for the job-shop scheduling problem," Economics Working Papers 1050, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Rego, César & Duarte, Renato, 2009. "A filter-and-fan approach to the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 194(3), pages 650-662, May.
    3. Jain, A. S. & Meeran, S., 1999. "Deterministic job-shop scheduling: Past, present and future," European Journal of Operational Research, Elsevier, vol. 113(2), pages 390-434, March.
    4. Christoph Schuster, 2006. "No-wait Job Shop Scheduling: Tabu Search and Complexity of Subproblems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 63(3), pages 473-491, July.
    5. F. Guerriero, 2008. "Hybrid Rollout Approaches for the Job Shop Scheduling Problem," Journal of Optimization Theory and Applications, Springer, vol. 139(2), pages 419-438, November.
    6. Francis Sourd & Wim Nuijten, 2000. "Multiple-Machine Lower Bounds for Shop-Scheduling Problems," INFORMS Journal on Computing, INFORMS, vol. 12(4), pages 341-352, November.
    7. Blazewicz, Jacek & Domschke, Wolfgang & Pesch, Erwin, 1996. "The job shop scheduling problem: Conventional and new solution techniques," European Journal of Operational Research, Elsevier, vol. 93(1), pages 1-33, August.
    8. Demirkol, Ebru & Mehta, Sanjay & Uzsoy, Reha, 1998. "Benchmarks for shop scheduling problems," European Journal of Operational Research, Elsevier, vol. 109(1), pages 137-141, August.
    9. Edzard Weber & Anselm Tiefenbacher & Norbert Gronau, 2019. "Need for Standardization and Systematization of Test Data for Job-Shop Scheduling," Data, MDPI, vol. 4(1), pages 1-21, February.
    10. Diarmuid Grimes & Emmanuel Hebrard, 2015. "Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 268-284, May.
    11. G I Zobolas & C D Tarantilis & G Ioannou, 2009. "A hybrid evolutionary algorithm for the job shop scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(2), pages 221-235, February.
    12. Kurowski, Krzysztof & Pecyna, Tomasz & Slysz, Mateusz & Różycki, Rafał & Waligóra, Grzegorz & Wȩglarz, Jan, 2023. "Application of quantum approximate optimization algorithm to job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 310(2), pages 518-528.
    13. Jelke J. Hoorn, 2018. "The Current state of bounds on benchmark instances of the job-shop scheduling problem," Journal of Scheduling, Springer, vol. 21(1), pages 127-128, February.
    14. Da Col, Giacomo & Teppan, Erich C., 2022. "Industrial-size job shop scheduling with constraint programming," Operations Research Perspectives, Elsevier, vol. 9(C).
    15. Murovec, Boštjan, 2015. "Job-shop local-search move evaluation without direct consideration of the criterion’s value," European Journal of Operational Research, Elsevier, vol. 241(2), pages 320-329.
    16. Habibeh Nazif, 2015. "Solving Job Shop Scheduling Problem Using an Ant Colony Algorithm," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 5(5), pages 261-268, May.
    17. Hamed Piroozfard & Kuan Yew Wong & Adnan Hassan, 2016. "A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems," Journal of Optimization, Hindawi, vol. 2016, pages 1-13, April.
    18. Varela, Ramiro & Vela, Camino R. & Puente, Jorge & Gomez, Alberto, 2003. "A knowledge-based evolutionary strategy for scheduling problems with bottlenecks," European Journal of Operational Research, Elsevier, vol. 145(1), pages 57-71, February.
    19. El-Bouri, A. & Azizi, N. & Zolfaghari, S., 2007. "A comparative study of a new heuristic based on adaptive memory programming and simulated annealing: The case of job shop scheduling," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1894-1910, March.
    20. Zapfel, G. & Wasner, M., 2000. "A heuristic solution concept for a generalized machine sequencing problem with an application to radiator manufacturing," International Journal of Production Economics, Elsevier, vol. 68(2), pages 199-213, November.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rug:rugwps:10/679. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Nathalie Verhaeghe (email available below). General contact details of provider: https://edirc.repec.org/data/ferugbe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.