Capacitated disassembly scheduling with random demand and operation time

Abstract

The disassembly activity, regarding as the crucial stage in recycling operations, has attracted increasing focus owing to the significance of eco-economics and environmental issues. This paper examines the capacitated disassembly scheduling with demand and disassembly operation time uncertainty consideration, which is the problem of determining the quantity of the end-of-life (EOL) products (root item) to be disassembled while satisfying recycling market. The addressed problem is formulated as a novel stochastic programming model and a hybrid genetic-based algorithm (HGA) is proposed to derive the best solution. To deal with the uncertain demand of disassembled parts/modules (leaf item) and the disassembly operation time, the fixed sample size (FSS) sampling strategy is employed and embedded into the designed heuristic algorithm, lunched by the Monte Carlo Simulation. The numerical instances under different scales are performed, and results show that the developed HGA manifests good performance in terms of accuracy and efficiency.

Keywords:

• Capacitated disassembly scheduling
• random demand and operation time
• disassembly yield
• fixed sample size (FSS) sampling strategy
• HGA

Disclosure statement

No potential conflict of interest was reported by the authors.

Acknowledgements

We thank anonymous referees and editors for their valuable comments and constructive advice. This research is supported by the following projects: the Henan Province Philosophy and Social Science Planning Project (grant no. 2020CZH012), the National Natural Science Foundation of China for Young Scholars (grant no. 71803181), the Think-tank Programme of Henan Science & Technology (grant no. HNKJZK-2020-41C), and the Scientific Research Starting Fund from ZZULI (grant no. 2018BSJJ071).