IDEAS home Printed from https://ideas.repec.org/a/wly/jnljam/v2012y2012i1n651310.html

Solving Flexible Job‐Shop Scheduling Problem Using Gravitational Search Algorithm and Colored Petri Net

Author

Listed:
  • Behnam Barzegar
  • Homayun Motameni
  • Hossein Bozorgi

Abstract

Scheduled production system leads to avoiding stock accumulations, losses reduction, decreasing or even eliminating idol machines, and effort to better benefitting from machines for on time responding customer orders and supplying requested materials in suitable time. In flexible job‐shop scheduling production systems, we could reduce time and costs by transferring and delivering operations on existing machines, that is, among NP‐hard problems. The scheduling objective minimizes the maximal completion time of all the operations, which is denoted by Makespan. Different methods and algorithms have been presented for solving this problem. Having a reasonable scheduled production system has significant influence on improving effectiveness and attaining to organization goals. In this paper, new algorithm were proposed for flexible job‐shop scheduling problem systems (FJSSP‐GSPN) that is based on gravitational search algorithm (GSA). In the proposed method, the flexible job‐shop scheduling problem systems was modeled by color Petri net and CPN tool and then a scheduled job was programmed by GSA algorithm. The experimental results showed that the proposed method has reasonable performance in comparison with other algorithms.

Suggested Citation

  • Behnam Barzegar & Homayun Motameni & Hossein Bozorgi, 2012. "Solving Flexible Job‐Shop Scheduling Problem Using Gravitational Search Algorithm and Colored Petri Net," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
  • Handle: RePEc:wly:jnljam:v:2012:y:2012:i:1:n:651310
    DOI: 10.1155/2012/651310
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2012/651310
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2012/651310?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Sabuncuoglu, I. & Bayiz, M., 1999. "Job shop scheduling with beam search," European Journal of Operational Research, Elsevier, vol. 118(2), pages 390-412, October.
    3. Eugeniusz Nowicki & Czeslaw Smutnicki, 1996. "A Fast Taboo Search Algorithm for the Job Shop Problem," Management Science, INFORMS, vol. 42(6), pages 797-813, June.
    4. M. R. Garey & D. S. Johnson & Ravi Sethi, 1976. "The Complexity of Flowshop and Jobshop Scheduling," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 117-129, May.
    5. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, 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. Miao Liu & Shouguang Wang & Zhiwu Li, 2013. "Supervisor Reconfiguration for Deadlock Prevention by Resources Reallocation," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    2. Juan-Ignacio Latorre-Biel & Emilio Jiménez-Macías & Mercedes Pérez de la Parte & Julio Blanco-Fernández & Eduardo Martínez-Cámara, 2014. "Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).

    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. 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.
    2. Rossi, Andrea, 2014. "Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships," International Journal of Production Economics, Elsevier, vol. 153(C), pages 253-267.
    3. Fernandez-Viagas, Victor & Talens, Carla & Prata, Bruno de Athayde, 2025. "A speed-up procedure and new heuristics for the classical job shop scheduling problem: A computational evaluation," European Journal of Operational Research, Elsevier, vol. 322(3), pages 783-794.
    4. 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.
    5. Mati, Yazid & Dauzère-Pérès, Stèphane & Lahlou, Chams, 2011. "A general approach for optimizing regular criteria in the job-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 212(1), pages 33-42, July.
    6. Bryan A. Norman & James C. Bean, 1999. "A genetic algorithm methodology for complex scheduling problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 46(2), pages 199-211, March.
    7. Paola Festa & Panos Pardalos, 2012. "Efficient solutions for the far from most string problem," Annals of Operations Research, Springer, vol. 196(1), pages 663-682, July.
    8. Shen, Liji & Buscher, Udo, 2012. "Solving the serial batching problem in job shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 14-26.
    9. Ganesan, Viswanath Kumar & Sivakumar, Appa Iyer, 2006. "Scheduling in static jobshops for minimizing mean flowtime subject to minimum total deviation of job completion times," International Journal of Production Economics, Elsevier, vol. 103(2), pages 633-647, October.
    10. Fowler, John W. & Mönch, Lars, 2022. "A survey of scheduling with parallel batch (p-batch) processing," European Journal of Operational Research, Elsevier, vol. 298(1), pages 1-24.
    11. Qingzheng Xu & Na Wang & Lei Wang & Wei Li & Qian Sun, 2021. "Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review," Mathematics, MDPI, vol. 9(8), pages 1-44, April.
    12. Xiao, Lei & Zhang, Xinghui & Tang, Junxuan & Zhou, Yaqin, 2020. "Joint optimization of opportunistic maintenance and production scheduling considering batch production mode and varying operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    13. Khalilpoor, Saeedeh & Kamran, Mehdi A. & Solimanpur, Maghsud, 2025. "Resilient COVID-19 vaccine supply chain: An optimization and simulation approach for multi-objective management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 201(C).
    14. 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.
    15. Wei Wang & Yaofeng Xu & Liguo Hou, 2019. "Optimal allocation of test times for reliability growth testing with interval-valued model parameters," Journal of Risk and Reliability, , vol. 233(5), pages 791-802, October.
    16. Bürgy, Reinhard & Bülbül, Kerem, 2018. "The job shop scheduling problem with convex costs," European Journal of Operational Research, Elsevier, vol. 268(1), pages 82-100.
    17. Jun Pei & Bayi Cheng & Xinbao Liu & Panos M. Pardalos & Min Kong, 2019. "Single-machine and parallel-machine serial-batching scheduling problems with position-based learning effect and linear setup time," Annals of Operations Research, Springer, vol. 272(1), pages 217-241, January.
    18. Christos Koulamas, 1997. "Decomposition and hybrid simulated annealing heuristics for the parallel‐machine total tardiness problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(1), pages 109-125, February.
    19. F. Stefanello & L. S. Buriol & M. J. Hirsch & P. M. Pardalos & T. Querido & M. G. C. Resende & M. Ritt, 2017. "On the minimization of traffic congestion in road networks with tolls," Annals of Operations Research, Springer, vol. 249(1), pages 119-139, February.
    20. Drexl, Andreas & Salewski, Frank, 1996. "Distribution Requirements and Compactness Constraints in School Timetabling. Part II: Methods," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 384, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.

    More about this item

    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:wly:jnljam:v:2012:y:2012:i:1:n:651310. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/4185 .

    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.