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Robust machine layout design under dynamic environment: dynamic customer demand and machine maintenance

Lookup NU author(s): Dr Pupong Pongcharoen, Professor Christian Hicks

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Abstract

The layout of manufacturing facilities has a large impact on manufacturing performance. The layout design process produces a block plan that shows the relative positioning of resources that can be developed into a detailed layout drawing. The total materials handling distance is commonly used for measuring material flow. Manufacturing systems are subject to external and internal uncertainties including demand and machine breakdowns. Uncertainty and the rerouting of material flows have an impact on the material handling distance. No previous research has integrated robust machine layout design through multiple periods of dynamic demand with machine maintenance planning. This paper presents a robust machine layout design tool that minimises the material flow distance using a Genetic Algorithm (GA), taking into account demand uncertainty and machine maintenance. Experiments were conducted using eleven benchmark datasets that considered three scenarios: preventive maintenance (PM), corrective maintenance (CM) and both PM and CM. The results were analysed statistically. The effect of several maintenance scenarios including the ratio of the number of machines with period-based PM (PPM) to the number with production quantity-based PM (QPM), the percentage of machines with CM (%CM), and a combination of PMM/QPM ratios and %CM on material flow distance were examined. The results show that designing robust layouts considering maintenance resulted in shorter material flow distances. The distance was decreased by 30.91%, 9.8%, and 20.7% for the PM, CM, and both PM/CM scenarios, respectively. The PPM/QPM ratios, %CM, and a combination of PPM/QPM and %CM had significantly resulted in the material flow distance on almost all datasets.

Publication metadata

Author(s): Vitayasak S, Pongcharoen P, Hicks C

Publication type: Article

Publication status: Published

Journal: Expert Systems with Applications

Year: 2019

Volume: 3

Print publication date: 01/09/2019

Online publication date: 04/08/2019

Acceptance date: 06/08/2019

Date deposited: 08/08/2019

ISSN (print): 0957-4174

ISSN (electronic): 1873-6793

Publisher: Pergamon Press

URL: https://doi.org/10.1016/j.eswax.2019.100015

DOI: 10.1016/j.eswax.2019.100015

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