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Multi-objective genetic algorithm for energy-efficient job shop scheduling

Author

Listed:
  • Gökan May
  • Bojan Stahl
  • Marco Taisch
  • Vittal Prabhu

Abstract

The paper investigates the effects of production scheduling policies aimed towards improving productive and environmental performances in a job shop system. A green genetic algorithm allows the assessment of multi-objective problems related to sustainability. Two main considerations have emerged from the application of the algorithm. First, the algorithm is able to achieve a semi-optimal makespan similar to that obtained by the best of other methods but with a significantly lower total energy consumption. Second, the study demonstrated that the worthless energy consumption can be reduced significantly by employing complex energy-efficient machine behaviour policies.

Suggested Citation

  • Gökan May & Bojan Stahl & Marco Taisch & Vittal Prabhu, 2015. "Multi-objective genetic algorithm for energy-efficient job shop scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7071-7089, December.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:23:p:7071-7089
    DOI: 10.1080/00207543.2015.1005248
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    Citations

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    Cited by:

    1. Min Dai & Ziwei Zhang & Adriana Giret & Miguel A. Salido, 2019. "An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints," Sustainability, MDPI, vol. 11(11), pages 1-23, May.
    2. Xu Zhang & Hua Zhang & Jin Yao, 2020. "Multi-Objective Optimization of Integrated Process Planning and Scheduling Considering Energy Savings," Energies, MDPI, vol. 13(23), pages 1-31, November.
    3. Tao Ren & Yan Zhang & Shuenn-Ren Cheng & Chin-Chia Wu & Meng Zhang & Bo-yu Chang & Xin-yue Wang & Peng Zhao, 2020. "Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates," Mathematics, MDPI, vol. 8(8), pages 1-25, July.
    4. Masmoudi, Oussama & Delorme, Xavier & Gianessi, Paolo, 2019. "Job-shop scheduling problem with energy consideration," International Journal of Production Economics, Elsevier, vol. 216(C), pages 12-22.
    5. Zhang, Liping & Tang, Qiuhua & Wu, Zhengjia & Wang, Fang, 2017. "Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops," Energy, Elsevier, vol. 138(C), pages 210-227.
    6. Beck, Fabian G. & Biel, Konstantin & Glock, Christoph H., 2019. "Integration of energy aspects into the economic lot scheduling problem," International Journal of Production Economics, Elsevier, vol. 209(C), pages 399-410.
    7. João M. R. C. Fernandes & Seyed Mahdi Homayouni & Dalila B. M. M. Fontes, 2022. "Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review," Sustainability, MDPI, vol. 14(10), pages 1-34, May.
    8. Abdolreza Roshani & Massimo Paolucci & Davide Giglio & Melissa Demartini & Flavio Tonelli & Maxim A. Dulebenets, 2023. "The capacitated lot-sizing and energy efficient single machine scheduling problem with sequence dependent setup times and costs in a closed-loop supply chain network," Annals of Operations Research, Springer, vol. 321(1), pages 469-505, February.
    9. Yicong Gao & Qirui Wang & Yixiong Feng & Hao Zheng & Bing Zheng & Jianrong Tan, 2018. "An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line," Energies, MDPI, vol. 11(5), pages 1-18, May.
    10. May, Gökan & Stahl, Bojan & Taisch, Marco, 2016. "Energy management in manufacturing: Toward eco-factories of the future – A focus group study," Applied Energy, Elsevier, vol. 164(C), pages 628-638.
    11. Chen Peng & Tao Peng & Yi Zhang & Renzhong Tang & Luoke Hu, 2018. "Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop," Energies, MDPI, vol. 11(12), pages 1-15, December.
    12. Heydar, Mojtaba & Mardaneh, Elham & Loxton, Ryan, 2022. "Approximate dynamic programming for an energy-efficient parallel machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 302(1), pages 363-380.
    13. Zhou, Shengchao & Jin, Mingzhou & Du, Ni, 2020. "Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times," Energy, Elsevier, vol. 209(C).
    14. Anghinolfi, Davide & Paolucci, Massimo & Ronco, Roberto, 2021. "A bi-objective heuristic approach for green identical parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 289(2), pages 416-434.
    15. Zhihao Zhao & Yue Sun & Aiguo Patrick Hu & Xin Dai & Chunsen Tang, 2016. "Energy Link Optimization in a Wireless Power Transfer Grid under Energy Autonomy Based on the Improved Genetic Algorithm," Energies, MDPI, vol. 9(9), pages 1-16, August.
    16. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    17. 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.
    18. Li, Lei & Huang, Haihong & Zou, Xiang & Zhao, Fu & Li, Guishan & Liu, Zhifeng, 2021. "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, Elsevier, vol. 286(C).
    19. Favi, Claudio & Marconi, Marco & Mandolini, Marco & Germani, Michele, 2022. "Sustainable life cycle and energy management of discrete manufacturing plants in the industry 4.0 framework," Applied Energy, Elsevier, vol. 312(C).
    20. 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.
    21. Panda, Debashish & Ramteke, Manojkumar, 2019. "Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 68-82.

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