Capacitated disassembly scheduling with random demand and operation time

Fuli Zhou; Yandong He; Panpan Ma; Ming Lim; Saurabh Pratap

doi:10.1080/01605682.2021.1911603

Capacitated disassembly scheduling with random demand and operation time

Fuli Zhou, Yandong He, Panpan Ma, Ming Lim, Saurabh Pratap

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13 Citations (Scopus)

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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.

Original language	English
Pages (from-to)	1362-1378
Number of pages	17
Journal	Journal of the Operational Research Society
Volume	73
Issue number	6
Early online date	17 May 2021
DOIs	https://doi.org/10.1080/01605682.2021.1911603
Publication status	Published - 3 Jun 2022

Bibliographical note

This is an Accepted Manuscript version of the following article, accepted for publication in Journal of the Operational Research Society. Zhou, F, He, Y, Ma, P, Lim, M & Pratap, S 2021, 'Capacitated disassembly scheduling with random demand and operation time', Journal of the Operational Research Society, vol. (In-Press), pp. (In-Press)..

It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Funder

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).

Keywords

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

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10.1080/01605682.2021.1911603

Post-PrintAccepted author manuscript, 3.34 MBLicence: CC BY-NC

Cite this

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title = "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. ",

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PY - 2022/6/3

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N2 - 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.

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Capacitated disassembly scheduling with random demand and operation time

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Keywords

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