A Heuristic Method to Solve an Energy-Efficient Unrelated Parallel Machines Scheduling Problem With Learning Effectand Deteriorating Jobs

This study proposes a mixed-integer programming model for an energy-efficient unrelated parallel machines scheduling under uncertainty. The proposed model considers some features like weight for jobs, learning effect, deteriorating jobs and the machine eligibility constraint. The objective function of the proposed model minimizes total weighted completion times and total energy consumption. In this research, the robust optimization method is applied to cope with the uncertainty. Due to NP-hardness ofthe research problem, a heuristic algorithm is developed to solve the proposed model in reasonable time. Then, the problem solved in different sizes and the efficiency of the developed algorithm is examined. Finally, sensitivity analysis is carried out on some parameters of the proposed model and the results are reported. Results show that the developed model can obtain optimal/near optimal solutions in less CPU time than exact method.

Keywords:

Energy-efficient scheduling , Unrelated parallel machines , Heuristic method , Learning effect , Deteriorating jobs