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Multi-Objective Flexible Flow Shop Scheduling Problem Considering Variable Processing Time due to Renewable Energy

Author

Listed:
  • Xiuli Wu

    (Department of Logistics Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Xianli Shen

    (Department of Logistics Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Qi Cui

    (Department of Logistics Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China)

Abstract

Renewable energy is an alternative to non-renewable energy to reduce the carbon footprint of manufacturing systems. Finding out how to make an alternative energy-efficient scheduling solution when renewable and non-renewable energy drives production is of great importance. In this paper, a multi-objective flexible flow shop scheduling problem that considers variable processing time due to renewable energy (MFFSP-VPTRE) is studied. First, the optimization model of the MFFSP-VPTRE is formulated considering the periodicity of renewable energy and the limitations of energy storage capacity. Then, a hybrid non-dominated sorting genetic algorithm with variable local search (HNSGA-II) is proposed to solve the MFFSP-VPTRE. An operation and machine-based encoding method is employed. A low-carbon scheduling algorithm is presented. Besides the crossover and mutation, a variable local search is used to improve the offspring’s Pareto set. The offspring and the parents are combined and those that dominate more are selected to continue evolving. Finally, two groups of experiments are carried out. The results show that the low-carbon scheduling algorithm can effectively reduce the carbon footprint under the premise of makespan optimization and the HNSGA-II outperforms the traditional NSGA-II and can solve the MFFSP-VPTRE effectively and efficiently.

Suggested Citation

  • Xiuli Wu & Xianli Shen & Qi Cui, 2018. "Multi-Objective Flexible Flow Shop Scheduling Problem Considering Variable Processing Time due to Renewable Energy," Sustainability, MDPI, vol. 10(3), pages 1-30, March.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:841-:d:136645
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    References listed on IDEAS

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    Cited by:

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    3. Luay Elkhidir & Khalid Khan & Mohammad Al-Muhaini & Muhammad Khalid, 2022. "Enhancing Transient Response and Voltage Stability of Renewable Integrated Microgrids," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
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    5. Neufeld, Janis S. & Schulz, Sven & Buscher, Udo, 2023. "A systematic review of multi-objective hybrid flow shop scheduling," European Journal of Operational Research, Elsevier, vol. 309(1), pages 1-23.
    6. 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.
    7. Zhongwei Zhang & Lihui Wu & Tao Peng & Shun Jia, 2018. "An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment," Sustainability, MDPI, vol. 11(1), pages 1-21, December.
    8. Nailiang Li & Caihong Feng, 2021. "Research on Machining Workshop Batch Scheduling Incorporating the Completion Time and Non-Processing Energy Consumption Considering Product Structure," Energies, MDPI, vol. 14(19), pages 1-26, September.
    9. 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.
    10. Wenzhu Liao & Tong Wang, 2018. "Promoting Green and Sustainability: A Multi-Objective Optimization Method for the Job-Shop Scheduling Problem," Sustainability, MDPI, vol. 10(11), pages 1-19, November.

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