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An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints

Author

Listed:
  • Min Dai

    (College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China)

  • Ziwei Zhang

    (College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China)

  • Adriana Giret

    (Departamento de Sistemas Informáticos y Computación/AI2, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

  • Miguel A. Salido

    (Departamento de Sistemas Informáticos y Computación/AI2, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

Abstract

Nowadays, the manufacturing industry faces the challenge of reducing energy consumption and the associated environmental impacts. Production scheduling is an effective approach for energy-savings management. During the entire workshop production process, both the processing and transportation operations consume large amounts of energy. To reduce energy consumption, an energy-efficient job-shop scheduling problem (EJSP) with transportation constraints was proposed in this paper. First, a mixed-integer programming model was established to minimize both the comprehensive energy consumption and makespan in the EJSP. Then, an enhanced estimation of distribution algorithm (EEDA) was developed to solve the problem. In the proposed algorithm, an estimation of distribution algorithm was employed to perform the global search and an improved simulated annealing algorithm was designed to perform the local search. Finally, numerical experiments were implemented to analyze the performance of the EEDA. The results showed that the EEDA is a promising approach and that it can solve EJSP effectively and efficiently.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3085-:d:236050
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    References listed on IDEAS

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    1. 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.
    2. Peter J. M. van Laarhoven & Emile H. L. Aarts & Jan Karel Lenstra, 1992. "Job Shop Scheduling by Simulated Annealing," Operations Research, INFORMS, vol. 40(1), pages 113-125, February.
    3. Qi Wang & Dunbing Tang & Shipei Li & Jun Yang & Miguel A. Salido & Adriana Giret & Haihua Zhu, 2019. "An Optimization Approach for the Coordinated Low-Carbon Design of Product Family and Remanufactured Products," Sustainability, MDPI, vol. 11(2), pages 1-22, January.
    4. Lacomme, Philippe & Larabi, Mohand & Tchernev, Nikolay, 2013. "Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles," International Journal of Production Economics, Elsevier, vol. 143(1), pages 24-34.
    5. Yuquan Meng & Yuhang Yang & Haseung Chung & Pil-Ho Lee & Chenhui Shao, 2018. "Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review," Sustainability, MDPI, vol. 10(12), pages 1-28, December.
    6. Fuqing Zhao & Zhongshi Shao & Junbiao Wang & Chuck Zhang, 2016. "A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1039-1060, February.
    7. Gahm, Christian & Denz, Florian & Dirr, Martin & Tuma, Axel, 2016. "Energy-efficient scheduling in manufacturing companies: A review and research framework," European Journal of Operational Research, Elsevier, vol. 248(3), pages 744-757.
    8. Liu, Ying & Dong, Haibo & Lohse, Niels & Petrovic, Sanja, 2016. "A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance," International Journal of Production Economics, Elsevier, vol. 179(C), pages 259-272.
    9. 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.
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    Cited by:

    1. 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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