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Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm

Author

Listed:
  • Wenjie Wang

    (Shandong University
    Zhejiang University)

  • Guangdong Tian

    (Shandong University
    Zhejiang University)

  • Gang Yuan

    (Jilin University
    National University of Singapore)

  • Duc Truong Pham

    (University of Birmingham)

Abstract

This article studies the scheduling problem for a remanufacturing system with parallel disassembly workstations, parallel flow-shop-type reprocessing lines and parallel reassembly workstations. The problem is formulated as a multi-objective optimization problem which contains both energy consumption and makespan to be addressed using an improved multi-objective invasive weed optimization (MOIWO) algorithm. Two vectors regarding workstation assignment and operation scheduling jointly form a solution. A hybrid initialization strategy is utilized to improve the solution quality and the Sigma method is adopted to rate each solution. A novel seed spatial dispersal mechanism is introduced and four designed mutation operations cooperate to enhance search ability. A group of numerical experiments and a practical case involving the disassembly of transmission devices are carried out and the results validate the effectiveness of the MOIWO algorithm for the considered problem compared with existing methods.

Suggested Citation

  • Wenjie Wang & Guangdong Tian & Gang Yuan & Duc Truong Pham, 2023. "Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1065-1083, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01837-5
    DOI: 10.1007/s10845-021-01837-5
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    1. Yaping Ren & Xinyu Lu & Hongfei Guo & Zhaokang Xie & Haoyang Zhang & Chaoyong Zhang, 2023. "A Review of Combinatorial Optimization Problems in Reverse Logistics and Remanufacturing for End-of-Life Products," Mathematics, MDPI, vol. 11(2), pages 1-24, January.
    2. Georgios K. Koulinas & Panagiotis D. Paraschos & Dimitrios E. Koulouriotis, 2024. "A machine learning framework for explainable knowledge mining and production, maintenance, and quality control optimization in flexible circular manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 737-759, September.

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