IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i10p1463-d1390837.html
   My bibliography  Save this article

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Author

Listed:
  • Binzi Xu

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Kai Xu

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Baolin Fei

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Dengchao Huang

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Liang Tao

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Yan Wang

    (School of IoT and Engineering, Jiangnan University, Wuxi 214122, China)

Abstract

Considering the requirements of the actual production scheduling process, the utilization of the genetic programming hyper-heuristic (GPHH) approach to automatically design dispatching rules (DRs) has recently emerged as a popular optimization approach. However, the decision objects and decision environments for routing and sequencing decisions are different in the dynamic flexible job shop scheduling problem (DFJSSP), leading to different required feature information. Traditional algorithms that allow these two types of scheduling decisions to share one common feature set are not conducive to the further optimization of the evolved DRs, but instead introduce redundant and unnecessary search attempts for algorithm optimization. To address this, some related studies have focused on customizing the feature sets for both routing and sequencing decisions through feature selection when solving single-objective problems. While being effective in reducing the search space, the selected feature sets also diminish the diversity of the obtained DRs, ultimately impacting the optimization performance. Consequently, this paper proposes an improved GPHH with dual feature weight sets for the multi-objective energy-efficient DFJSSP, which includes two novel feature weight measures and one novel hybrid population adjustment strategy. Instead of selecting suitable features, the proposed algorithm assigns appropriate weights to the features based on their multi-objective contribution, which could provide directional guidance to the GPHH while ensuring the search space. Experimental results demonstrate that, compared to existing studies, the proposed algorithm can significantly enhance the optimization performance and interpretability of energy-efficient DRs.

Suggested Citation

  • Binzi Xu & Kai Xu & Baolin Fei & Dengchao Huang & Liang Tao & Yan Wang, 2024. "Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets," Mathematics, MDPI, vol. 12(10), pages 1-24, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1463-:d:1390837
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/10/1463/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/10/1463/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Gmys, Jan & Mezmaz, Mohand & Melab, Nouredine & Tuyttens, Daniel, 2020. "A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 284(3), pages 814-833.
    3. Hankun Zhang & Borut Buchmeister & Xueyan Li & Robert Ojstersek, 2023. "An Efficient Metaheuristic Algorithm for Job Shop Scheduling in a Dynamic Environment," Mathematics, MDPI, vol. 11(10), pages 1-24, May.
    4. S. S. Panwalkar & Wafik Iskander, 1977. "A Survey of Scheduling Rules," Operations Research, INFORMS, vol. 25(1), pages 45-61, February.
    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. Drexl, Andreas & Kolisch, Rainer, 1991. "Produktionsplanung und -steuerung bei Einzel- und Kleinserienfertigung," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 281, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    2. Anurag Agarwal & Varghese S. Jacob & Hasan Pirkul, 2006. "An Improved Augmented Neural-Network Approach for Scheduling Problems," INFORMS Journal on Computing, INFORMS, vol. 18(1), pages 119-128, February.
    3. Parlakturk, Ali & Kumar, Sunil, 2004. "Self-Interested Routing in Queueing Networks," Research Papers 1782r, Stanford University, Graduate School of Business.
    4. Bierwirth, C. & Kuhpfahl, J., 2017. "Extended GRASP for the job shop scheduling problem with total weighted tardiness objective," European Journal of Operational Research, Elsevier, vol. 261(3), pages 835-848.
    5. Mobin, Mohammadsadegh & Li, Zhaojun & Cheraghi, S. Hossein & Wu, Gongyu, 2019. "An approach for design Verification and Validation planning and optimization for new product reliability improvement," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    6. M. Vimala Rani & M. Mathirajan, 2020. "Performance evaluation of due-date based dispatching rules in dynamic scheduling of diffusion furnace," OPSEARCH, Springer;Operational Research Society of India, vol. 57(2), pages 462-512, June.
    7. P Chen & C-C Wu & W-C Lee, 2006. "A bi-criteria two-machine flowshop scheduling problem with a learning effect," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(9), pages 1113-1125, September.
    8. Chauvet, Fabrice & Levner, Eugene & Meyzin, Leonid K. & Proth, Jean-Marie, 2000. "On-line scheduling in a surface treatment system," European Journal of Operational Research, Elsevier, vol. 120(2), pages 382-392, January.
    9. Zoghby, Jeriad & Wesley Barnes, J. & Hasenbein, John J., 2005. "Modeling the reentrant job shop scheduling problem with setups for metaheuristic searches," European Journal of Operational Research, Elsevier, vol. 167(2), pages 336-348, December.
    10. Valls, Vicente & Angeles Perez, M. & Sacramento Quintanilla, M., 1998. "A tabu search approach to machine scheduling," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 277-300, April.
    11. Alejandra Duenas & Dobrila Petrovic, 2008. "An approach to predictive-reactive scheduling of parallel machines subject to disruptions," Annals of Operations Research, Springer, vol. 159(1), pages 65-82, March.
    12. 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.
    13. Guinet, Alain & Legrand, Marie, 1998. "Reduction of job-shop problems to flow-shop problems with precedence constraints," European Journal of Operational Research, Elsevier, vol. 109(1), pages 96-110, August.
    14. Monaci, Marta & Agasucci, Valerio & Grani, Giorgio, 2024. "An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents," European Journal of Operational Research, Elsevier, vol. 312(3), pages 910-926.
    15. Chen, Haoxun & Luh, Peter B., 2003. "An alternative framework to Lagrangian relaxation approach for job shop scheduling," European Journal of Operational Research, Elsevier, vol. 149(3), pages 499-512, September.
    16. Qingmiao Liao & Jianjun Yang & Yong Zhou, 2019. "Sustainable Scheduling of an Automatic Pallet Changer System by Multi-Objective Evolutionary Algorithm with First Piece Inspection," Sustainability, MDPI, vol. 11(5), pages 1-24, March.
    17. Land, Martin & Gaalman, Gerard, 1996. "Workload control concepts in job shops A critical assessment," International Journal of Production Economics, Elsevier, vol. 46(1), pages 535-548, December.
    18. 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.
    19. G-C Lee & Y-D Kim & J-G Kim & S-H Choi, 2003. "A dispatching rule-based approach to production scheduling in a printed circuit board manufacturing system," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(10), pages 1038-1049, October.
    20. Böttcher, Jan & Drexl, Andreas & Kolisch, Rainer & Salewski, Frank, 1996. "Project scheduling under partially renewable resource constraints," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 398, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.

    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:gam:jmathe:v:12:y:2024:i:10:p:1463-:d:1390837. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.