A hybrid metaheuristic algorithm for a profit-oriented and energy-efficient disassembly sequencing problem
AffiliationUniv Arizona, Dept Syst & Ind Engn
MetadataShow full item record
PublisherPERGAMON-ELSEVIER SCIENCE LTD
CitationLu, Q., Ren, Y., Jin, H., Meng, L., Li, L., Zhang, C., & Sutherland, J. W. (2020). A hybrid metaheuristic algorithm for a profit-oriented and energy-efficient disassembly sequencing problem. Robotics and Computer-Integrated Manufacturing, 61, 101828.
Rights© 2019 Elsevier Ltd. All rights reserved.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractValue 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.
Note24 month embargo; published online: 3 July 2019
VersionFinal accepted manuscript
SponsorsFunds for National Natural Science Foundation of ChinaNational Natural Science Foundation of China ; International Cooperation and Exchange of the National Natural Science Foundation of ChinaNational Natural Science Foundation of China ; U.S. National Science FoundationNational Science Foundation (NSF) ; China Scholarship CouncilChina Scholarship Council