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Hybrid Differential Evolution Algorithm and Adaptive Large Neighborhood Search to Solve Parallel Machine Scheduling to Minimize Energy Consumption in Consideration of Machine-Load Balance Problems

Author

Listed:
  • Rujapa Nanthapodej

    (Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Pingtung 912, Taiwan)

  • Cheng-Hsiang Liu

    (Department of Industrial Management, National Pingtung University of Science and Technology, Pingtung 912, Taiwan)

  • Krisanarach Nitisiri

    (Department of Industrial Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Sirorat Pattanapairoj

    (Department of Industrial Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

Abstract

Environmental and economic considerations create a challenge for manufacturers. The main priorities for production planning in environmentally friendly manufacturing industries are reducing energy consumption and improving productivity by balancing machine load. This paper focuses on parallel machine scheduling to minimize energy consumption (PMS_ENER), which is an indicator of environmental sustainability when considering machine-load balance problems. A mathematical model was formulated to solve the proposed problem and tested using a set of problem groups. The findings indicated that the mathematical model could find an optimal solution within a limited calculation time for small problems. For medium and large problems, the mathematical model could also find the optimal solution within a limited calculation time, but worse than all metaheuristics. However, finding an optimal solution for a larger problem is time-consuming. Thus, a novel method, a hybrid differential evolution algorithm with adaptive large neighborhood search (HyDE-ALNS), is presented to solve large-scale PMS_ENER. The new mutation and recombination formula for the differential evolution (DE) algorithm proposed in this article obtained promising results. By using the HyDE-ALNS, we improved the solution quality by 0.22%, 7.21%, and 12.01% compared with a modified DE (MDE-3) for small, medium, and large problems respectively. In addition, five new removal methods were designed to implement in ALNS and achieve optimal solution quality.

Suggested Citation

  • Rujapa Nanthapodej & Cheng-Hsiang Liu & Krisanarach Nitisiri & Sirorat Pattanapairoj, 2021. "Hybrid Differential Evolution Algorithm and Adaptive Large Neighborhood Search to Solve Parallel Machine Scheduling to Minimize Energy Consumption in Consideration of Machine-Load Balance Problems," Sustainability, MDPI, vol. 13(10), pages 1-25, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5470-:d:554125
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    References listed on IDEAS

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