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Partial disassembly line balancing under uncertainty: robust optimisation models and an improved migrating birds optimisation algorithm

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journal contribution
posted on 2020-03-18, 15:20 authored by Qinxin Xiao, Xiuping Guo, Dong Li
A partial disassembly line balancing problem under uncertainty is studied in this paper, which concerns the allocation of a sequence of tasks to workstations such that the overall profit is maximised. We consider the processing time uncertainty and develop robust solutions to accommodate it. The problem is formulated as a non-linear robust integer program, which is then converted into an equivalent linear program. Due to the intractability of such problems, the exact algorithms are only applicable to small-scale instances. We develop an improved migrating birds optimisation algorithm. Two enhancement techniques are proposed. The first one finds the optimal number of tasks to be performed for each sequence rather than random selection used in the literature; while the second one exploits the specific problem structure to construct effective neighbourhoods. The numerical results show the strong performance of our proposal compared to CPLEX and the improved gravitational search algorithm (IGSA), especially for large-scale problems. Moreover, the enhancement due to the proposed techniques is obvious across all instances considered.

Funding

National Natural Science Foundation of China [grant number 71471151]

History

School

  • Business and Economics

Department

  • Business

Published in

International Journal of Production Research

Volume

59

Issue

10

Pages

2977-2995

Publisher

Taylor & Francis

Version

  • AM (Accepted Manuscript)

Rights holder

© Informa UK Limited, trading as Taylor & Francis Group

Publisher statement

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 27 March 2020, available online: http://www.tandfonline.com/10.1080/00207543.2020.1744765.

Acceptance date

2020-03-12

Publication date

2020-03-27

Copyright date

2021

ISSN

0020-7543

eISSN

1366-588X

Language

  • en

Depositor

Dr Dong Li. Deposit date: 17 March 2020

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