IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p2622-d1054375.html
   My bibliography  Save this article

A Multiobjective Optimization Approach for Multiobjective Hybrid Flowshop Green Scheduling with Consistent Sublots

Author

Listed:
  • Weiwei Wang

    (School of Computer Science, Liaocheng University, Liaocheng 252000, China)

  • Biao Zhang

    (School of Computer Science, Liaocheng University, Liaocheng 252000, China)

  • Baoxian Jia

    (School of Computer Science, Liaocheng University, Liaocheng 252000, China)

Abstract

Hybrid flowshop scheduling problems are encountered in many real-world manufacturing scenarios. With increasingly fierce market competition, the production mode of multiple varieties and small batches has gradually been accepted by enterprises, where the technology of lot streaming is widely used. Meanwhile, green criteria, such as energy consumption and carbon emissions, have attracted increasing attention to improving protection awareness. With these motivations, this paper studies a multiobjective hybrid flowshop green scheduling problem with consistent sublots (MOHFGSP_CS), aiming to minimize two objectives, i.e., makespan and total energy consumption, simultaneously. To solve this complex problem, we first formulate a novel multiobjective optimization model. However, due to the NP-hard nature of the problem, the model is computationally prohibitive as the problem scale increases. Thus, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is proposed. There are three phases in this algorithm: the VND-based employed bee phase, the adjustment weight onlooker bee phase, and the population interaction scout bee phase. In the experimental study, various small-scale and large-scale instances are collected to verify the effectiveness of the multiobjective optimization model and the MDABC. Comprehensive computational comparisons and statistical analysis show that the developed strategies and MDABC show superior performance.

Suggested Citation

  • Weiwei Wang & Biao Zhang & Baoxian Jia, 2023. "A Multiobjective Optimization Approach for Multiobjective Hybrid Flowshop Green Scheduling with Consistent Sublots," Sustainability, MDPI, vol. 15(3), pages 1-29, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2622-:d:1054375
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/2622/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/3/2622/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kim, J-S. & Kang, S-H. & Lee, S. M., 1997. "Transfer batch scheduling for a two-stage flowshop with identical parallel machines at each stage," Omega, Elsevier, vol. 25(5), pages 547-555, October.
    2. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
    3. Ding, Jian-Ya & Song, Shiji & Wu, Cheng, 2016. "Carbon-efficient scheduling of flow shops by multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 248(3), pages 758-771.
    4. Pan, Quan-Ke & Gao, Liang & Li, Xin-Yu & Gao, Kai-Zhou, 2017. "Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times," Applied Mathematics and Computation, Elsevier, vol. 303(C), pages 89-112.
    5. Guo-Zhong Fu & Tianda Yu & Wei Li & Qiang Deng & Bo Yang, 2021. "A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, September.
    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. Zhi Li & Ray Y. Zhong & Ali Vatankhah Barenji & J. J. Liu & C. X. Yu & George Q. Huang, 2021. "Bi-objective hybrid flow shop scheduling with common due date," Operational Research, Springer, vol. 21(2), pages 1153-1178, June.
    2. Chengshuai Li & Biao Zhang & Yuyan Han & Yuting Wang & Junqing Li & Kaizhou Gao, 2022. "Energy-Efficient Hybrid Flowshop Scheduling with Consistent Sublots Using an Improved Cooperative Coevolutionary Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-27, December.
    3. Missaoui, Ahmed & Ruiz, Rubén, 2022. "A parameter-Less iterated greedy method for the hybrid flowshop scheduling problem with setup times and due date windows," European Journal of Operational Research, Elsevier, vol. 303(1), pages 99-113.
    4. Fang Wang & Yunqing Rao & Chaoyong Zhang & Qiuhua Tang & Liping Zhang, 2016. "Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes," Sustainability, MDPI, vol. 8(8), pages 1-20, August.
    5. Bozorgirad, Mir Abbas & Logendran, Rasaratnam, 2013. "Bi-criteria group scheduling in hybrid flowshops," International Journal of Production Economics, Elsevier, vol. 145(2), pages 599-612.
    6. Zoltán Varga & Pál Simon, 2014. "Examination Of Scheduling Methods For Production Systems," Advanced Logistic systems, University of Miskolc, Department of Material Handling and Logistics, vol. 8(1), pages 111-120, December.
    7. Weng, Wei & Fujimura, Shigeru, 2012. "Control methods for dynamic time-based manufacturing under customized product lead times," European Journal of Operational Research, Elsevier, vol. 218(1), pages 86-96.
    8. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    9. Weiwei Cui & Biao Lu, 2020. "A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    10. Yong Wang & Yuting Wang & Yuyan Han, 2023. "A Variant Iterated Greedy Algorithm Integrating Multiple Decoding Rules for Hybrid Blocking Flow Shop Scheduling Problem," Mathematics, MDPI, vol. 11(11), pages 1-25, May.
    11. Santini, Alberto & Bartolini, Enrico & Schneider, Michael & Greco de Lemos, Vinicius, 2021. "The crop growth planning problem in vertical farming," European Journal of Operational Research, Elsevier, vol. 294(1), pages 377-390.
    12. 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.
    13. Weiya Zhong & Yun Shi, 2018. "Two-stage no-wait hybrid flowshop scheduling with inter-stage flexibility," Journal of Combinatorial Optimization, Springer, vol. 35(1), pages 108-125, January.
    14. Pan, Quan-Ke & Gao, Liang & Li, Xin-Yu & Gao, Kai-Zhou, 2017. "Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times," Applied Mathematics and Computation, Elsevier, vol. 303(C), pages 89-112.
    15. 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.
    16. Jin Xu & Natarajan Gautam, 2020. "On competitive analysis for polling systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(6), pages 404-419, September.
    17. Perez-Gonzalez, Paz & Framinan, Jose M., 2024. "A review and classification on distributed permutation flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 1-21.
    18. Figielska, Ewa, 2014. "A heuristic for scheduling in a two-stage hybrid flowshop with renewable resources shared among the stages," European Journal of Operational Research, Elsevier, vol. 236(2), pages 433-444.
    19. Alexis Robbes & Yannick Kergosien & Virginie André & Jean-Charles Billaut, 2022. "Efficient heuristics to minimize the total tardiness of chemotherapy drug production and delivery," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 785-820, September.
    20. Hasani, Ali & Hosseini, Seyed Mohammad Hassan, 2020. "A bi-objective flexible flow shop scheduling problem with machine-dependent processing stages: Trade-off between production costs and energy consumption," Applied Mathematics and Computation, Elsevier, vol. 386(C).

    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:jsusta:v:15:y:2023:i:3:p:2622-:d:1054375. 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.