فهرست نشریات علمی دسترسی آزاد ایران

– 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 time to complete jobs aligns with reducing energy consumption. Therefore, efficient scheduling is one of the important concerns in industrial units. This research aims to minimize joint energy consumption and total job completion time in a flow-shop environment, considering speed level and tool wear level constraints. The increased speed level reduces the time to complete 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 a 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.