A hybrid metaheuristic algorithm for a profit-oriented and energy-efficient disassembly sequencing problem

Qi Lu, Yaping Ren, Hongyue Jin, Leilei Meng, Lei Li, Chaoyong Zhang, John W. Sutherland

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


Value recovery from end-of-life products plays a key role in sustainability and circular economy, which starts with disassembly of products into components for reuse, remanufacturing, or recycling. As the process is often complex, a disassembly sequencing problem (DSP) studies how to optimally disassemble products considering the physical constraints between subassemblies/disassembly tasks for maximum profit. With a growing attention on energy conservation, this paper addresses a profit-oriented and energy-efficient DSP (PEDSP), whereby not only the profit is maximized, but also energy consumption is accounted as an important decision criterion. In this work, a disassembly AND/OR graph (DAOG) is used to model a disassembly diagram for a product, in which the ‘AND’ and ‘OR’ relations illustrate precedence relationships between subassemblies. Based on the DAOG, we propose a hybrid multi-objective metaheuristic that integrates an artificial bee colony algorithm, a non-dominated sorting procedure, and a variable neighborhood search approach to solve the PEDSP for Pareto solutions. The proposed method is applied to real-world cases (i.e., a simple ballpoint pen and a relatively complex radio) and compared with other multi-objective algorithms. The results indicate that our method can quickly produce a Pareto front that outperforms the alternative approaches.

Original languageEnglish (US)
Article number101828
JournalRobotics and Computer-Integrated Manufacturing
StatePublished - Feb 2020
Externally publishedYes


  • AND/OR graph
  • Disassembly sequencing
  • Energy consumption
  • Multi-objective metaheuristic
  • Value recovery

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • General Mathematics
  • Computer Science Applications
  • Industrial and Manufacturing Engineering


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