Abstract
Disassembly sequence planning (DSP) can effectively increase the disassembly efficiency, shorten the disassembly cycle, reduce disassembly costs, and reduce environmental hazards of end-of-life (EOL) products, playing an important role in manufacturing industries. Thus, it is urgent to propose an approach to solve the DSP problem. DSP is a famous NP-hard combinatorial optimization problem. As the size of components increases, exact algorithms can hardly obtain the optimal disassembly sequence. Therefore, we propose a promising intelligence algorithm, modified grey wolf optimizer (MGWO), to solve the DSP problem. MGWO inherits the main idea of the hierarchy and hunting mechanism of the original grey wolf optimizer (GWO). Three new operators are designed in MGWO to ensure the feasibility of solutions under the complex constraint of disassembly precedence. The feasible solution generator (FSG) is designed to obtain feasible disassembly sequences, the neighborhood search operator (NSO) is developed to make wolves (solutions) self-evolving, and the guided search operator (GSO) is used to make the wolf group guided by three leaders of wolves. Two engineering cases are applied to validate the effectiveness of the proposed operators. Then, they and two real-world applications are used to compare the MGWO with other reported methods. The results demonstrate that MGWO can solve the DSP problem effectively.
Highlights
With the renewal of modern technology and the shortening of product life, an enormous amount of end-of-life (EOL) products has been generated
A modified grey wolf optimizer (MGWO) algorithm is proposed for solving the Disassembly sequence planning (DSP) problem
Two engineering cases are employed to test the effectiveness of these operators. They and one real-world application are employed to test the validity of MGWO
Summary
With the renewal of modern technology and the shortening of product life, an enormous amount of end-of-life (EOL) products has been generated. Kim and Lee [10] developed an integer programming model to represent disassembly sequences, and proposed a branch and bound method for obtaining the lower and upper bounds of the DSP problem. Tian et al [11] designed an AND/OR graph for DSP considering the uncertainty of part quality and variable disassembly cost They proposed an intelligent algorithm combining artificial bee colony (ABC) with fuzzy simulation for solving the problem. Guo et al [15] presented a dualobjective optimization model to represent the selective disassembly considering multi-resource constraints, and developed a scatter search algorithm to solve the model. This paper designs a modified grey wolf optimizer (MGWO) to solve the DSP problem. NSO and GSO are designed for updating disassembly sequences Based on these three operators, MGWO can converge fast and search efficiently.
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