Flow-shop scheduling problem with tool changes and tool wear to minimize energy consumption

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Semnan University, Semnan, Iran

2 Department of Industrial Engineering, Semnan University, Semnan, Iran.

3 Department of industrial engineering, Faculty of engineering, Semnan University, Semnan, Iran

10.22070/jqepo.2025.18387.1270

Abstract

Nowadays, most production units seek to minimize the amount of energy consumption due to the resulting economic pressures and the shortage of energy carriers such as electricity or fossil fuels. On the other hand, minimizing the total completion time of jobs is also in line with reducing energy consumption. Therefore, efficient scheduling is one of the important concerns in industrial units. This research aims to minimize the joint energy consumption and total job completion time in the flow-shop environment by considering the speed level and tool wear level constraints. Here, the increased speed level reduces the completion time of the job and, on the other hand, increases energy consumption. Moreover, the increase in the machine speed causes an increase in the level of wear and eventually tool change. Since the flow-shop scheduling problem is NP-hard, it cannot be solved in a short time on a large scale. To cope with this issue, we solve this multi-objective optimization problem with two well-known metaheuristic methods, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The experimental results reveal that MOPSO outperforms NSGA-II in terms of MOCV and MID metrics. Conversely, NSGA-II outperforms MOPSO in terms of CPU time.

Keywords

Journal of Quality Engineering and Production Optimization

Articles in Press, Accepted Manuscript
Available Online from 27 May 2025

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