Campus Units

Industrial and Manufacturing Systems Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

6-1-2021

Journal or Book Title

International Journal of Production Research

Research Focus Area(s)

​Operations Research, Advanced Manufacturing Research

DOI

10.1080/00207543.2021.1931978

Abstract

Sequencing decisions in mixed-model assembly lines are complicated by various uncertainty factors. This paper addresses a real-life uncertainty factor identified in a manufacturer of large vehicles, by modelling unreliable part delivery and quality. Stochastic optimisation is applied to find sequencing policies that improve the on-time performance of its mixed-model assembly lines. As schedulers have different levels of risk aversion, a risk-averse programme is further presented to protect against the decision maker’s chosen fraction of worst scenarios. Computational studies with Progressive Hedging as the solution method, and its lower bounding approach, demonstrate the high quality of resulting sequencing decisions and the time efficiency of the solution method.

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on June 1, 2021, available online at DOI: 10.1080/00207543.2021.1931978. Posted with permission.

Copyright Owner

Informa UK Limited

Language

en

File Format

application/pdf

Available for download on Wednesday, June 01, 2022

Published Version

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