LAUSR

Disassembly planning aims to search the best disassembly sequences of a given obsolete/used product in terms of economic and environmental performances. A practical disassembly process may face great uncertainty owing to various unpredictable factors. To handle it, researchers have addressed the stochastic cost and time problems of product disassembly. In reality, the uncertain environment of product disassembly is associated with both randomness and fuzziness. Besides uncertain disassembly cost and time, the quality of disassembled components/parts in a process has uncertainty and thus needs to be assessed via expert opinions/subjects. To do so, this paper presents a new AND/OR-graph-based disassembly sequence planning problem by considering uncertain component quality and varying disassembly operational cost. Important disassembly planning models are built on the basis of different disassembly criteria. A novel hybrid intelligent algorithm integrating fuzzy simulation and artificial bee colony is proposed to solve them. Its effectiveness is well illustrated through several numerical cases and comparison with a prior method, i.e., fuzzy-simulation-based genetic algorithm.Note to Practitioners—This paper deals with the uncertainty management problem of product disassembly. It builds some fuzzy programming models for product disassembly and proposes a hybrid intelligent algorithm integrating fuzzy simulation and artificial bee colony to solve them. Previously, such a problem was handled through a methodology based on stochastic planning, which was ineffective without considering the fuzzy characteristic of completing a disassembly task. The goal of this paper is to analyze the disassembly uncertainty feature from the perspective of fuzzy programming. Both theoretical and simulation results demonstrate that the proposed approach is highly effective. The obtained results can help decision-makers better determine a disassembly process of a used/returned/obsolete product.