IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v180y2010i1p165-19610.1007-s10479-008-0501-4.html
   My bibliography  Save this article

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Author

Listed:
  • Jacomine Grobler
  • Andries Engelbrecht
  • Schalk Kok
  • Sarma Yadavalli

Abstract

This paper investigates the application of particle swarm optimization (PSO) to the multi-objective flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and machine down time. To achieve this goal, alternative particle representations and problem mapping mechanisms were implemented within the PSO paradigm. This resulted in the development of four PSO-based heuristics. Benchmarking on real customer data indicated that using the priority-based representation resulted in a significant performance improvement over the existing rule-based algorithms commonly used to solve this problem. Additional investigation into algorithm scalability led to the development of a priority-based differential evolution algorithm. Apart from the academic significance of the paper, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability, and overall competitive advantage. Copyright Springer Science+Business Media, LLC 2010

Suggested Citation

  • Jacomine Grobler & Andries Engelbrecht & Schalk Kok & Sarma Yadavalli, 2010. "Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time," Annals of Operations Research, Springer, vol. 180(1), pages 165-196, November.
    • Handle: RePEc:spr:annopr:v:180:y:2010:i:1:p:165-196:10.1007/s10479-008-0501-4
    DOI: 10.1007/s10479-008-0501-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-008-0501-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-008-0501-4?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. Mascis, Alessandro & Pacciarelli, Dario, 2002. "Job-shop scheduling with blocking and no-wait constraints," European Journal of Operational Research, Elsevier, vol. 143(3), pages 498-517, December.
    2. Potts, Chris N. & Kovalyov, Mikhail Y., 2000. "Scheduling with batching: A review," European Journal of Operational Research, Elsevier, vol. 120(2), pages 228-249, January.
    3. Brucker, Peter & Kramer, Andreas, 1996. "Polynomial algorithms for resource-constrained and multiprocessor task scheduling problems," European Journal of Operational Research, Elsevier, vol. 90(2), pages 214-226, April.
    4. Bertel, S. & Billaut, J. -C., 2004. "A genetic algorithm for an industrial multiprocessor flow shop scheduling problem with recirculation," European Journal of Operational Research, Elsevier, vol. 159(3), pages 651-662, December.
    5. 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.
    6. 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.
    7. Jansen, Klaus & Mastrolilli, Monaldo & Solis-Oba, Roberto, 2005. "Approximation schemes for job shop scheduling problems with controllable processing times," European Journal of Operational Research, Elsevier, vol. 167(2), pages 297-319, December.
    8. Chung, Daeyoung & Lee, Kichang & Shin, Kitae & Park, Jinwoo, 2005. "A new approach to job shop scheduling problems with due date constraints considering operation subcontracts," International Journal of Production Economics, Elsevier, vol. 98(2), pages 238-250, November.
    9. Carlo Meloni & Dario Pacciarelli & Marco Pranzo, 2004. "A Rollout Metaheuristic for Job Shop Scheduling Problems," Annals of Operations Research, Springer, vol. 131(1), pages 215-235, October.
    10. 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.
    11. Anghinolfi, Davide & Paolucci, Massimo, 2009. "A new discrete particle swarm optimization approach for the single-machine total weighted tardiness scheduling problem with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 193(1), pages 73-85, February.
    12. Kacem, Imed & Hammadi, Slim & Borne, Pierre, 2002. "Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 245-276.
    13. 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.
    14. Verhoeven, M. G. A., 1998. "Tabu search for resource-constrained scheduling," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 266-276, April.
    15. Tasgetiren, M. Fatih & Liang, Yun-Chia & Sevkli, Mehmet & Gencyilmaz, Gunes, 2007. "A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1930-1947, March.
    16. Cavory, G. & Dupas, R. & Goncalves, G., 2005. "A genetic approach to solving the problem of cyclic job shop scheduling with linear constraints," European Journal of Operational Research, Elsevier, vol. 161(1), pages 73-85, February.
    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. Qiu, Rui & Xu, Jiuping & Zeng, Ziqiang & Chen, Xin & Wang, Yinhai, 2022. "Carbon tax policy-induced air travel carbon emission reduction and biofuel usage in China," Journal of Air Transport Management, Elsevier, vol. 103(C).
    2. Qiu, Rui & Xu, Jiuping & Ke, Ruimin & Zeng, Ziqiang & Wang, Yinhai, 2020. "Carbon pricing initiatives-based bi-level pollution routing problem," European Journal of Operational Research, Elsevier, vol. 286(1), pages 203-217.
    3. Shesh Narayan Sahu & Yuvraj Gajpal & Swapan Debbarma, 2018. "Two-agent-based single-machine scheduling with switchover time to minimize total weighted completion time and makespan objectives," Annals of Operations Research, Springer, vol. 269(1), pages 623-640, October.
    4. Ali Hosseinabadi & Hajar Siar & Shahaboddin Shamshirband & Mohammad Shojafar & Mohd Nasir, 2015. "Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises," Annals of Operations Research, Springer, vol. 229(1), pages 451-474, June.
    5. Alper Türkyılmaz & Özlem Şenvar & İrem Ünal & Serol Bulkan, 2020. "A research survey: heuristic approaches for solving multi objective flexible job shop problems," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1949-1983, December.
    6. Guangchen Wang & Xinyu Li & Liang Gao & Peigen Li, 2022. "An effective multi-objective whale swarm algorithm for energy-efficient scheduling of distributed welding flow shop," Annals of Operations Research, Springer, vol. 310(1), pages 223-255, March.
    7. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.

    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. 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.
    2. 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.
    3. Zhu, Jie & Li, Xiaoping & Wang, Qian, 2009. "Complete local search with limited memory algorithm for no-wait job shops to minimize makespan," European Journal of Operational Research, Elsevier, vol. 198(2), pages 378-386, October.
    4. Mogali, Jayanth Krishna & Barbulescu, Laura & Smith, Stephen F., 2021. "Efficient primal heuristic updates for the blocking job shop problem," European Journal of Operational Research, Elsevier, vol. 295(1), pages 82-101.
    5. Tamssaouet, Karim & Dauzère-Pérès, Stéphane & Knopp, Sebastian & Bitar, Abdoul & Yugma, Claude, 2022. "Multiobjective optimization for complex flexible job-shop scheduling problems," European Journal of Operational Research, Elsevier, vol. 296(1), pages 87-100.
    6. A. G. Leeftink & R. J. Boucherie & E. W. Hans & M. A. M. Verdaasdonk & I. M. H. Vliegen & P. J. Diest, 2018. "Batch scheduling in the histopathology laboratory," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 171-197, June.
    7. Sanja Petrovic & Carole Fayad & Dobrila Petrovic & Edmund Burke & Graham Kendall, 2008. "Fuzzy job shop scheduling with lot-sizing," Annals of Operations Research, Springer, vol. 159(1), pages 275-292, March.
    8. 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.
    9. Zhou, Hong & Cheung, Waiman & Leung, Lawrence C., 2009. "Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm," European Journal of Operational Research, Elsevier, vol. 194(3), pages 637-649, May.
    10. Pham, Dinh-Nguyen & Klinkert, Andreas, 2008. "Surgical case scheduling as a generalized job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1011-1025, March.
    11. 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.
    12. Pisut Pongchairerks, 2019. "A Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem," Complexity, Hindawi, vol. 2019, pages 1-11, March.
    13. 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.
    14. Hurink, Johann & Knust, Sigrid, 2005. "Tabu search algorithms for job-shop problems with a single transport robot," European Journal of Operational Research, Elsevier, vol. 162(1), pages 99-111, April.
    15. Chen, Yin-Yann & Cheng, Chen-Yang & Wang, Li-Chih & Chen, Tzu-Li, 2013. "A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry," International Journal of Production Economics, Elsevier, vol. 141(1), pages 66-78.
    16. Murovec, Bostjan & Suhel, Peter, 2004. "A repairing technique for the local search of the job-shop problem," European Journal of Operational Research, Elsevier, vol. 153(1), pages 220-238, February.
    17. Allahverdi, Ali & Ng, C.T. & Cheng, T.C.E. & Kovalyov, Mikhail Y., 2008. "A survey of scheduling problems with setup times or costs," European Journal of Operational Research, Elsevier, vol. 187(3), pages 985-1032, June.
    18. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.
    19. Li-Ning Xing & Ying-Wu Chen & Ke-Wei Yang, 2011. "Multi-population interactive coevolutionary algorithm for flexible job shop scheduling problems," Computational Optimization and Applications, Springer, vol. 48(1), pages 139-155, January.
    20. Reinhard Bürgy, 2017. "A neighborhood for complex job shop scheduling problems with regular objectives," Journal of Scheduling, Springer, vol. 20(4), pages 391-422, August.

    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:annopr:v:180:y:2010:i:1:p:165-196:10.1007/s10479-008-0501-4. 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.