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    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 25 June 2019, available online: https://www.tandfonline.com/doi/abs/10.1080/00207543.2019.1630771.

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    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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The use of finite loading to guide short-term capacity adjustments in make-to-order job shops: An assessment by simulation

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Published
<mark>Journal publication date</mark>1/06/2020
<mark>Journal</mark>International Journal of Production Research
Issue number12
Volume58
Number of pages16
Pages (from-to)3554-3569
Publication StatusPublished
Early online date25/06/19
<mark>Original language</mark>English

Abstract

Although there is a broad literature on capacity management, there has been only limited attention on how to support short-term capacity control decisions, especially in high-variety make-to-order shops. While finite loading has been identified as a potential means of guiding capacity adjustments, the actual performance impact of this solution has not been adequately assessed. Using a simulation model of a make-to-order job shop, we compare the performance impact of four different forward and backward finite loading methods and a load trigger method recently presented in the literature. Results confirm the potential of finite loading to improve performance when compared to a general capacity increase. Yet all four methods are outperformed by the load trigger method. The capacity adjustments made under finite loading methods are determined by individual jobs and their properties. This may lead to no adjustments despite an overload period (e.g. if a job has a long due date but only one overload station in its routing) or to unnecessary adjustments when there is no overload (e.g. if a large job has a tight due date). This finding draws into question the use of finite loading altogether and reinforces the importance of the load trigger method.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 25 June 2019, available online: https://www.tandfonline.com/doi/abs/10.1080/00207543.2019.1630771.