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Lookup NU author(s): Dr Pupong Pongcharoen, Professor Christian Hicks
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Taylor & Francis, 2019.
For re-use rights please refer to the publisher's terms and conditions.
The scheduling literature is extensive, but much of this work is theoretical and does not capture the complexity of real world systems. Capital goods companies produce products with deep and complex product structures, each of which requires the coordination of jobbing, batch, flow and assembly processes. Many components require numerous operations on multiple machines. Integrated scheduling problems simultaneously consider two or more simultaneous decisions. Previous production scheduling research in the capital goods industry has neglected maintenance scheduling and used metaheuristics with stochastic search that cannot guarantee an optimal solution. This paper presents a novel mixed integer linear programming (MILP) model for simultaneously solving the integrated production and preventive maintenance scheduling problem in the capital goods industry, which was tested using data from a collaborating company. The objective was to minimise total costs including: tardiness and earliness penalty costs; component and assembly holding costs; preventive maintenance costs; and setup, production, transfer and production idle time costs. Thus, the objective function and problem formulation were more extensive than previous research. The tool was successfully tested using data obtained from a collaborating company. It was found that the company’s total cost could be reduced by up to 63.5%.
Author(s): Chansombat S, Pongcharoen P, Hicks C
Publication type: Article
Publication status: Published
Journal: International Journal of Production Research
Year: 2019
Volume: 57
Issue: 1
Pages: 61-82
Online publication date: 16/04/2018
Acceptance date: 23/03/2018
Date deposited: 23/03/2018
ISSN (print): 0020-7543
ISSN (electronic): 1366-588X
Publisher: Taylor & Francis
URL: https://doi.org/10.1080/00207543.2018.1459923
DOI: 10.1080/00207543.2018.1459923
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