Developing and solving the multi-objective flexible and sustainable job shop scheduling problem with reverse flow and job rotation considerations in uncertain situations

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Arsalan  Shojaei ¹ (Department of Industrial Engineering, Parand Branch, Islamic Azad University, Parand, Iran)
Davood  Jafari ² (Department of Industrial Engineering, Parand Branch, Islamic Azad University, Parand, Iran)
Mehran  Khalag ³ (Department of Industrial Engineering, Parand Branch, Islamic Azad University, Parand, Iran)
Parshang  Dokohaki ⁴ (Department of Industrial Engineering, Parand Branch, Islamic Azad University, Parand, Iran)

Received: 2022-07-18 Accepted : 2023-12-14 Published : 2024-09-01

Keywords: Flexible job shop, Scheduling, Job Rotation, Uncertain, The whale optimization algorithm (WOA),

Abstract :

Flexible job shop scheduling problem (FJSP) has received a lot of attention in recent years, but the important point is that this field of study can be subject to many assumptions and lots of innovations can be considered. One of these can be reverse flow, which has been overlooked in many studies, while its effect on the cost and time of construction is undeniable. Other areas such as job rotation as well as issues related to sustainability can be of particular importance in this area and have not been reviewed in previous researches. Therefore, the present study seeks to provide a model to optimize the multi-objective flexible job shop scheduling problem concerning the issues of sustainability with reverse flow and job rotation considerations. For this purpose, a multi-objective mathematical scheduling model is developed, the first goal of which is to minimize the construction time and the second goal is to minimize the issues related to sustainability. To solve the model, two methods were used: Sensitivity analysis and meta-heuristic. The whale optimization algorithm (WOA) was employed in the meta-heuristic method. The results of the implementation of WOA indicate the efficiency of the proposed algorithm, while the findings of the sensitivity analysis also point to the effect of research innovations on the objective functions of the problem.

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