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Carbon-efficient scheduling of flow shops by multi-objective optimization

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  • Ding, Jian-Ya
  • Song, Shiji
  • Wu, Cheng

Abstract

Recently, there has been an increasing concern on the carbon efficiency of the manufacturing industry. Since the carbon emissions in the manufacturing sector are directly related to the energy consumption, an effective way to improve carbon efficiency in an industrial plant is to design scheduling strategies aiming at reducing the energy cost of production processes. In this paper, we consider a permutation flow shop (PFS) scheduling problem with the objectives of minimizing the total carbon emissions and the makespan. To solve this multi-objective optimization problem, we first investigate the structural properties of non-dominated solutions. Inspired by these properties, we develop an extended NEH-Insertion Procedure with an energy-saving capability. The accelerating technique in Taillard’s method, which is commonly used for the ordinary flowshop problem, is incorporated into the procedure to improve the computational efficiency. Based on the extended NEH-Insertion Procedure, a multi-objective NEH algorithm (MONEH) and a modified multi-objective iterated greedy (MMOIG) algorithm are designed for solving the problem. Numerical computations show that the energy-saving module of the extended NEH-Insertion Procedure in MONEH and MMOIG significantly helps to improve the discovered front. In addition, systematic comparisons show that the proposed algorithms perform more effectively than other tested high-performing meta-heurisitics in searching for non-dominated solutions.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:248:y:2016:i:3:p:758-771
    DOI: 10.1016/j.ejor.2015.05.019
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    References listed on IDEAS

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    2. 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.
    3. Alvarez-Meaza, Izaskun & Zarrabeitia-Bilbao, Enara & Rio-Belver, Rosa-María & Garechana-Anacabe, Gaizka, 2021. "Green scheduling to achieve green manufacturing: Pursuing a research agenda by mapping science," Technology in Society, Elsevier, vol. 67(C).
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    5. Weiwei Cui & Lin Li & Zhiqiang Lu, 2019. "Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(2), pages 154-173, March.
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    7. Li, Wei & Nault, Barrie R. & Ye, Honghan, 2019. "Trade-off balancing in scheduling for flow shop production and perioperative processes," European Journal of Operational Research, Elsevier, vol. 273(3), pages 817-830.
    8. 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.
    9. Lingzhi Ren & Ning Yi & Zhiying Li & Zhaoxian Su, 2023. "Research on the Impact of Energy Saving and Emission Reduction Policies on Carbon Emission Efficiency of the Yellow River Basin: A Perspective of Policy Collaboration Effect," Sustainability, MDPI, vol. 15(15), pages 1-17, August.
    10. Liu, Aijun & Zhu, Qiuyun & Xu, Lei & Lu, Qiang & Fan, Youqing, 2021. "Sustainable supply chain management for perishable products in emerging markets: An integrated location-inventory-routing model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    11. Tianhua Jiang & Chao Zhang & Huiqi Zhu & Jiuchun Gu & Guanlong Deng, 2018. "Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm," Mathematics, MDPI, vol. 6(11), pages 1-16, October.
    12. 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.
    13. Deming Lei & Youlian Zheng & Xiuping Guo, 2017. "A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3126-3140, June.
    14. Foumani, Mehdi & Smith-Miles, Kate, 2019. "The impact of various carbon reduction policies on green flowshop scheduling," Applied Energy, Elsevier, vol. 249(C), pages 300-315.
    15. Markus Hilbert & Andreas Dellnitz & Andreas Kleine, 2023. "Production planning under RTP, TOU and PPA considering a redox flow battery storage system," Annals of Operations Research, Springer, vol. 328(2), pages 1409-1436, September.
    16. Wu, Xueqi & Che, Ada, 2019. "A memetic differential evolution algorithm for energy-efficient parallel machine scheduling," Omega, Elsevier, vol. 82(C), pages 155-165.
    17. 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).
    18. Jin Huang & Liangliang Jin & Chaoyong Zhang, 2017. "Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission," Sustainability, MDPI, vol. 9(10), pages 1-18, September.
    19. Wu, Xueqi & Che, Ada, 2020. "Energy-efficient no-wait permutation flow shop scheduling by adaptive multi-objective variable neighborhood search," Omega, Elsevier, vol. 94(C).
    20. Fan, Di & Lo, Chris K.Y. & Zhou, Yi, 2021. "Sustainability risk in supply bases: The role of complexity and coupling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    21. 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.
    22. Matthias Gerhard Wichmann & Christoph Johannes & Thomas Stefan Spengler, 2019. "An extension of the general lot-sizing and scheduling problem (GLSP) with time-dependent energy prices," Journal of Business Economics, Springer, vol. 89(5), pages 481-514, July.
    23. 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.

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