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Partial disassembly line balancing for energy consumption and profit under uncertainty

  • Kaipu Wang
  • , Xinyu Li
  • , Liang Gao*
  • , Akhil Garg
  • *Corresponding author for this work
  • Huazhong University of Science and Technology
  • Intelligent Manufacturing Key Laboratory of Ministry of Education
  • Shantou University

Research output: Contribution to journalArticlepeer-review

80 Citations (Scopus)

Abstract

The disassembly line is one of the most important ways to handle large quantities of waste electrical and electronic equipment efficiently. The disassembly line balancing is to assign disassembly tasks to each workstation reasonably while satisfying various constraints, which is one of the challenging topics in recent years. Considering the uncertainty, environmental protection and economic benefits of disassembly, this paper establishes a stochastic partial disassembly line balancing model that comprehensively evaluates the number of workstations, workload smoothness, energy consumption, and disassembly profit. In order to obtain high-quality feasible solutions, a new multi-objective discrete flower pollination algorithm is proposed. The method establishes four heuristic rules, discrete operations, and multi-objective processing methods combined with the characteristics of the problem. The effectiveness and application ability of the proposed model and method are verified by a disassembly example of a waste printer, and various disassembly schemes are obtained, which can provide guidance for decision makers to construct disassembly lines.

Original languageEnglish
Pages (from-to)235-251
Number of pages17
JournalRobotics and Computer-Integrated Manufacturing
Volume59
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Disassembly line balancing
  • Energy consumption
  • Flower pollination algorithm
  • Profit
  • Uncertainty

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