| Title |
A hybrid metaheuristic algorithm for a profit-oriented and energy-efficient disassembly sequencing problem |
| ID_Doc |
9599 |
| Authors |
Lu, Q; Ren, YP; Jin, HY; Meng, LL; Li, L; Zhang, CY; Sutherland, JW |
| Title |
A hybrid metaheuristic algorithm for a profit-oriented and energy-efficient disassembly sequencing problem |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.rcim.2019.101828 |
| Abstract |
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. |
| Author Keywords |
Value recovery; Disassembly sequencing; Energy consumption; AND/OR graph; Multi-objective metaheuristic |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000496834800004 |
| WoS Category |
Computer Science, Interdisciplinary Applications; Engineering, Manufacturing; Robotics |
| Research Area |
Computer Science; Engineering; Robotics |
| PDF |
http://manuscript.elsevier.com/S0736584518305489/pdf/S0736584518305489.pdf
|