An efficient metaheuristics for a sequence-dependent disassembly planning

Yaping Ren, Leilei Meng, Chaoyong Zhang, Fu Zhao, Ulah Saif, Aihua Huang, Gamini P. Mendis, John W. Sutherland

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


Disassembly planning (DP) is critical in remanufacturing and value recovery from end-of-life products and has attracted increasing attention due to the recent resurgence of research on circular economy. DP problem is NP-hard and its complexity increases exponentially with the size of problem. Sequence-dependent cost due to varying quality of the parts to be retrieved further increases the complexity of DP problems. This paper investigates the DP considering sequence-dependent costs among disassembly operations. A mathematical model is proposed with the objective to maximize the recovery profit using an AND/OR graph (AOG) subject to sequence-dependent costs. A novel two-phase heuristic method is developed to effectively generate feasible disassembly sequence according to the AOG in reasonable computation time. In addition, an improved genetic algorithm (IGA) is proposed to solve the problem, in combination with the presented two-phase heuristic. The performance of IGA is measured on a series of test problem instances against exact methods including CPLEX and an iterative method. Results indicate that IGA successfully find the near-optimal/optimal solutions and outperforms the other methods in terms of computation time. Finally, the proposed method is applied to compute the disassembly solution of a HG5-20 triaxial five speed mechanical transmission. Compared to the existing disassembly solutions of the transmission, the obtained solutions by IGA can shorten about 11% disassembly time and increase by approximately 7% recovery profit.

Original languageEnglish (US)
Article number118644
JournalJournal of Cleaner Production
StatePublished - Feb 1 2020

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Strategy and Management
  • Industrial and Manufacturing Engineering


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