Stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling via fruit fly optimization algorithm

Document Type

Article

Publication Date

1-1-2021

Abstract

Remanufacturing end-of-life (EOL) products is an important approach to yield great economic and environmental benefits. A remanufacturing process usually contains three shops, i.e., disassembly, processing and assembly shops. EOL products are dissembled into multiple components in a disassembly shop. Reusable components are reprocessed in a processing shop, and reassembled into their corresponding products in an assembly shop. To realize an overall optimization, we have to integrate them together when making decisions. In practice, a decision-maker usually has to optimize multiple criteria such as cost-related and service-oriented objectives. Additionally, we cannot accurately acquire the detail of EOL products due to their various usage processes. Therefore, multi-objective and uncertainty need to be considered simultaneously in an integrated disassembly-reprocessing-reassembly scheduling process. This work investigates a stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling problem to achieve the expected makespan and total tardiness minimization. To handle this problem, this work develops a multi-objective discrete fruit fly optimization algorithm incorporating a stochastic simulation approach. Its search techniques are designed according to this problem's features from five aspects, i.e., solution representation, heuristic decoding rules, smell-searching, vision-searching, and genetic-searching. Simulation experiments are conducted by adopting twenty-five instances to verify the performance of the proposed approach. Nondominated sorting genetic algorithm II, bi-objective multi-start simulated annealing method, and hybrid multi-objective discrete artificial bee colony are chosen for comparisons. By analyzing the results with three performance metrics, we can find that the proposed approach performs better on all the twenty-five instances than its peers. Specifically, it outperforms them by 6.45%–9.82%, 6.91%–17.64% and 1.19%–2.76% in terms of performance, respectively. The results confirm that the proposed approach can effectively and efficiently tackle the investigated problem.

Identifier

85089676010 (Scopus)

Publication Title

Journal of Cleaner Production

External Full Text Location

https://doi.org/10.1016/j.jclepro.2020.123364

ISSN

09596526

Volume

278

Grant

20170520135

Fund Ref

National Natural Science Foundation of China

This document is currently not available here.

Share

COinS