Journal of Applied Science and Engineering

Published by Tamkang University Press


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Muhammad Ridwan Andi PurnomoThis email address is being protected from spambots. You need JavaScript enabled to view it. and Hari Purnomo

Department of Industrial Engineering, Faculty of Industrial Technology, Universitas Islam Indonesia, Yogyakarta, Indonesia, 55584



Received: October 26, 2023
Accepted: February 5, 2024
Publication Date: April 13, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Supply chain system for innovative products typically adopts Make-to-Order (MTO) approach, which involves implementing a strict product delivery to customers. The production shop floor also needs additional time to foster innovation. Hence, the machine scheduling for the parts production must considering the delivery time to customers, while simultaneously minimising the scheduling makespan. The issue will be further compounded when the production shop floor has a restricted capacity for storing parts work-in-process and it is commonly known as no-wait flowshop system. Consequently, the machine scheduling must also consider minimising the waiting time for parts work-in-process in between machines. The aim of this study is to develop a machine scheduling optimisation model with those three objectives, and to the best for our knowledge, there is no existing studies on simultaneously optimising those objectives. In this case, there are 30 parts on 5 serial machines, resulting in a vast number of viable solutions. The optimisation model has been addressed by employing two intelligent algorithms, namely Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). The GA shows superior performance and its solution able to decrease the number of tardy parts, decrease the scheduling makespan by 15 minutes and decrease the overall waiting time for the parts work-in-process by 203 minutes. This enhancement has a beneficial effect on the supply chain system by enhancing the reputation for punctual delivery, providing additional time to do product innovations, and diminishing the likelihood of damage to parts work-in-process during the manufacturing process.

Keywords: no-wait flowshop; machines scheduling; make-to-order supply chain; genetic algorithm; particle swarm optimisation

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