IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v28y2017i8d10.1007_s10845-015-1065-1.html
   My bibliography  Save this article

Nested optimization method combining complex method and ant colony optimization to solve JSSP with complex associated processes

Author

Listed:
  • Yabo Luo

    (Wuhan University of Technology)

Abstract

Job Shop Scheduling Problem (JSSP) is one of classic combinatorial optimization problems and has a long research history. Modern job shop has following characteristics: increasingly complicated processes, small batch and personalized requirement, which lead to complex correlations among processes. Complex correlations of processes, involving nested correlations besides serial and parallel correlations, propose a new task for JSSP research. Decomposing JSSP into two nested sub problems of order of arranging processes and machine arrangement, this research integrates the traditional thought of complex method into the ant colony optimization (ACO) to develop a nested optimization method in order to solve the new task. This paper is divided into four parts: first, the model of JSSP with complex associated processes is constructed and the difficulties to solve which are analyzed and listed; second, the definition of “order of arranging processes” is originally proposed, based on which the mathematical model available for the complex method is developed, taking process starting time as design variables of the first level optimization. The steps of the first level optimization and the secondary nested flow chart are detailed with the demonstration of the effectiveness of the complex method’s iteration mechanism; third, based on the representation of features the order of arranging processes obtained by the first level optimization combined with the first-in first-out rule owns, the corresponding modified ACO algorithm, involving pheromone positive perception and reverse spreading mechanism, is put forward to realize the second level optimization, which result is taken as the objective function value of the complex vertex to realize the secondary nested optimization strategy; finally, taking plentiful JSSP with complex associated processes as study cases, a serial of comparative experiments are done respectively adopting the genetic algorithm, ACO algorithm, particle swarm optimization algorithm, some combinations of heuristic algorithms respectively in the nested two levels, and the proposed nested optimization method, and experiment results attest the reliability and superiority of the proposed method.

Suggested Citation

  • Yabo Luo, 2017. "Nested optimization method combining complex method and ant colony optimization to solve JSSP with complex associated processes," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1801-1815, December.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:8:d:10.1007_s10845-015-1065-1
    DOI: 10.1007/s10845-015-1065-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-015-1065-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-015-1065-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Prot, D. & Bellenguez-Morineau, O. & Lahlou, C., 2013. "New complexity results for parallel identical machine scheduling problems with preemption, release dates and regular criteria," European Journal of Operational Research, Elsevier, vol. 231(2), pages 282-287.
    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. 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.
    4. 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.
    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. Zheng Xiao & Zhenan Wang & Deng Liu & Hui Wang, 2022. "A path planning algorithm for PCB surface quality automatic inspection," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1829-1841, August.
    2. Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.

    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. 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.
    2. 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.
    3. 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.
    4. Da Col, Giacomo & Teppan, Erich C., 2022. "Industrial-size job shop scheduling with constraint programming," Operations Research Perspectives, Elsevier, vol. 9(C).
    5. Sels, Veronique & Craeymeersch, Kjeld & Vanhoucke, Mario, 2011. "A hybrid single and dual population search procedure for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 215(3), pages 512-523, December.
    6. S. David Wu & Eui-Seok Byeon & Robert H. Storer, 1999. "A Graph-Theoretic Decomposition of the Job Shop Scheduling Problem to Achieve Scheduling Robustness," Operations Research, INFORMS, vol. 47(1), pages 113-124, February.
    7. 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.
    8. A. Ozolins, 2020. "A new exact algorithm for no-wait job shop problem to minimize makespan," Operational Research, Springer, vol. 20(4), pages 2333-2363, December.
    9. Marco Pranzo & Dario Pacciarelli, 2016. "An iterated greedy metaheuristic for the blocking job shop scheduling problem," Journal of Heuristics, Springer, vol. 22(4), pages 587-611, August.
    10. Michael Pinedo & Marcos Singer, 1999. "A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop," Naval Research Logistics (NRL), John Wiley & Sons, vol. 46(1), pages 1-17, February.
    11. Mohammad Mahdi Ahmadian & Amir Salehipour, 2021. "The just-in-time job-shop scheduling problem with distinct due-dates for operations," Journal of Heuristics, Springer, vol. 27(1), pages 175-204, April.
    12. Bahman Naderi & Rubén Ruiz & Vahid Roshanaei, 2023. "Mixed-Integer Programming vs. Constraint Programming for Shop Scheduling Problems: New Results and Outlook," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 817-843, July.
    13. 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.
    14. 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.
    15. 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.
    16. Müller, David & Müller, Marcus G. & Kress, Dominik & Pesch, Erwin, 2022. "An algorithm selection approach for the flexible job shop scheduling problem: Choosing constraint programming solvers through machine learning," European Journal of Operational Research, Elsevier, vol. 302(3), pages 874-891.
    17. Ramesh Bollapragada & Norman M. Sadeh, 2004. "Proactive release procedures for just‐in‐time job shop environments, subject to machine failures," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(7), pages 1018-1044, October.
    18. 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.
    19. 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.
    20. Shahed Mahmud & Ripon K. Chakrabortty & Alireza Abbasi & Michael J. Ryan, 2022. "Switching strategy-based hybrid evolutionary algorithms for job shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1939-1966, October.

    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:spr:joinma:v:28:y:2017:i:8:d:10.1007_s10845-015-1065-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.