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A dynamic programming approach to a multi-objective disassembly line balancing problem

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posted on 2020-09-14, 13:44 authored by Yusha Zhou, Xiuping Guo, Dong Li
This paper concerns a disassembly line balancing problem (DLBP) in remanufacturing that aims to allocate a set of tasks to workstations to disassemble a product. We consider two objectives in the same time, i.e., minimising the number of workstations required and minimising the operating costs. A common approach to such problems is to covert the multiple objectives into a single one and solve the resulting problem with either exact or heuristic methods. However, the appropriate weights must be determined a priori, yet the results provide little insight on the trade-off between competing objectives. Moreover, DLBP problems are proven NP-complete and thus the solvable instances by exact methods are limited. To this end, we formulate the problem into a multi-objective dynamic program and prove the monotonicity property of both objective functions. A backward recursive algorithm is developed to efficiently generate all the non-dominated solutions. The numerical results show that our proposal is more efficient than alternative exact algorithms proposed in the literature and can handle much larger problem instances.

Funding

National Natural Science Foundation of China [grant number 71471151]

History

School

  • Business and Economics

Department

  • Business

Published in

Annals of Operations Research

Volume

311

Issue

2

Pages

921 - 944

Publisher

Springer (part of Springer Nature)

Version

  • AM (Accepted Manuscript)

Rights holder

© Springer Science+Business Media, LLC, part of Springer Nature

Publisher statement

This is a post-peer-review, pre-copyedit version of an article published in Annals of Operations Research. The final authenticated version is available online at: https://doi.org/10.1007/s10479-020-03797-0.

Acceptance date

2020-09-09

Publication date

2020-09-19

Copyright date

2020

ISSN

0254-5330

eISSN

1572-9338

Language

  • en

Depositor

Dr Dong Li . Deposit date: 10 September 2020

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