IDEAS home Printed from https://ideas.repec.org/a/spr/jsched/v24y2021i2d10.1007_s10951-020-00664-5.html
   My bibliography  Save this article

Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation

Author

Listed:
  • Mohamed Habib Zahmani

    (University of Mostaganem
    University of Oran 1 Ahmed Benbella)

  • Baghdad Atmani

    (University of Oran 1 Ahmed Benbella)

Abstract

In production planning and scheduling, data mining methods can be applied to transform the scheduling data into useful knowledge that can be used to improve planning/scheduling by enabling real-time decision-making. In this paper, a novel approach combining dispatching rules, a genetic algorithm, data mining, and simulation is proposed. The genetic algorithm (i) is used to solve scheduling problems, and the obtained solutions (ii) are analyzed in order to extract knowledge, which is then used (iii) to automatically assign in real-time different dispatching rules to machines based on the jobs in their respective queues. The experiments are conducted on a job shop scheduling problem with a makespan criterion. The obtained results from the computational study show that the proposed approach is a viable and effective approach for solving the job shop scheduling problem in real time.

Suggested Citation

  • Mohamed Habib Zahmani & Baghdad Atmani, 2021. "Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation," Journal of Scheduling, Springer, vol. 24(2), pages 175-196, April.
  • Handle: RePEc:spr:jsched:v:24:y:2021:i:2:d:10.1007_s10951-020-00664-5
    DOI: 10.1007/s10951-020-00664-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10951-020-00664-5
    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/s10951-020-00664-5?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. T. C. E. Cheng & Bo Peng & Zhipeng Lü, 2016. "A hybrid evolutionary algorithm to solve the job shop scheduling problem," Annals of Operations Research, Springer, vol. 242(2), pages 223-237, July.
    2. Chris N. Potts & Luk N. Van Wassenhove, 1985. "A Branch and Bound Algorithm for the Total Weighted Tardiness Problem," Operations Research, INFORMS, vol. 33(2), pages 363-377, April.
    3. Pu-Hai Chiang & Chau-Chen Torng, 2016. "A production planning and optimisation of multi-mode job shop scheduling problem for an avionics manufacturing plant," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 30(3/4), pages 179-195.
    4. 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.
    5. Olafsson, Sigurdur & Li, Xiaonan, 2010. "Learning effective new single machine dispatching rules from optimal scheduling data," International Journal of Production Economics, Elsevier, vol. 128(1), pages 118-126, November.
    6. Chen, Binchao & Matis, Timothy I., 2013. "A flexible dispatching rule for minimizing tardiness in job shop scheduling," International Journal of Production Economics, Elsevier, vol. 141(1), pages 360-365.
    7. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    8. Raghu, T. S. & Rajendran, Chandrasekharan, 1993. "An efficient dynamic dispatching rule for scheduling in a job shop," International Journal of Production Economics, Elsevier, vol. 32(3), pages 301-313, November.
    9. S. Karthikeyan & P. Asokan & S. Nickolas & Tom Page, 2012. "Solving flexible job-shop scheduling problem using hybrid particle swarm optimisation algorithm and data mining," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 26(1/2/3/4), pages 81-103.
    Full references (including those not matched with items on IDEAS)

    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. A. S. Xanthopoulos & D. E. Koulouriotis, 2018. "Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 69-91, January.
    2. Xiong, Hegen & Fan, Huali & Jiang, Guozhang & Li, Gongfa, 2017. "A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints," European Journal of Operational Research, Elsevier, vol. 257(1), pages 13-24.
    3. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).
    4. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
    5. Dung-Ying Lin & Tzu-Yun Huang, 2021. "A Hybrid Metaheuristic for the Unrelated Parallel Machine Scheduling Problem," Mathematics, MDPI, vol. 9(7), pages 1-20, April.
    6. Gréanne Leeftink & Erwin W. Hans, 2018. "Case mix classification and a benchmark set for surgery scheduling," Journal of Scheduling, Springer, vol. 21(1), pages 17-33, February.
    7. Og[breve]uz, Ceyda & Sibel Salman, F. & Bilgintürk YalçIn, Zehra, 2010. "Order acceptance and scheduling decisions in make-to-order systems," International Journal of Production Economics, Elsevier, vol. 125(1), pages 200-211, May.
    8. Jiang, Min & Huang, George Q., 2022. "Intralogistics synchronization in robotic forward-reserve warehouses for e-commerce last-mile delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    9. Yagiura, Mutsunori & Ibaraki, Toshihide, 1996. "The use of dynamic programming in genetic algorithms for permutation problems," European Journal of Operational Research, Elsevier, vol. 92(2), pages 387-401, July.
    10. Fei Luan & Zongyan Cai & Shuqiang Wu & Shi Qiang Liu & Yixin He, 2019. "Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm," Mathematics, MDPI, vol. 7(8), pages 1-17, August.
    11. Wang, Xiuli & Xie, Xingzi & Cheng, T.C.E., 2013. "Order acceptance and scheduling in a two-machine flowshop," International Journal of Production Economics, Elsevier, vol. 141(1), pages 366-376.
    12. Borgonjon, Tessa & Maenhout, Broos, 2022. "An exact approach for the personnel task rescheduling problem with task retiming," European Journal of Operational Research, Elsevier, vol. 296(2), pages 465-484.
    13. Tanja Mlinar & Philippe Chevalier, 2016. "Pooling heterogeneous products for manufacturing environments," 4OR, Springer, vol. 14(2), pages 173-200, June.
    14. Valente, Jorge M.S., 2007. "Improving the performance of the ATC dispatch rule by using workload data to determine the lookahead parameter value," International Journal of Production Economics, Elsevier, vol. 106(2), pages 563-573, April.
    15. J M S Valente & R A F S Alves, 2005. "Improved lower bounds for the early/tardy scheduling problem with no idle time," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 604-612, May.
    16. Lu Sun & Lin Lin & Haojie Li & Mitsuo Gen, 2019. "Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling," Mathematics, MDPI, vol. 7(4), pages 1-20, March.
    17. Bilge, Umit & Kurtulan, Mujde & Kirac, Furkan, 2007. "A tabu search algorithm for the single machine total weighted tardiness problem," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1423-1435, February.
    18. Chengbin Chu, 1992. "A branch‐and‐bound algorithm to minimize total tardiness with different release dates," Naval Research Logistics (NRL), John Wiley & Sons, vol. 39(2), pages 265-283, March.
    19. An, Youjun & Chen, Xiaohui & Hu, Jiawen & Zhang, Lin & Li, Yinghe & Jiang, Junwei, 2022. "Joint optimization of preventive maintenance and production rescheduling with new machine insertion and processing speed selection," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    20. Lin, Geng & Guan, Jian & Feng, Huibin, 2018. "An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 199-209.

    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:jsched:v:24:y:2021:i:2:d:10.1007_s10951-020-00664-5. 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.