A hybrid algorithm for unrelated parallel machines scheduling

Abstract

In this paper, a new hybrid algorithm based on multi-objective genetic algorithm (MOGA) using simulated annealing (SA) is proposed for scheduling unrelated parallel machines with sequencedependent setup times, varying due dates, ready times and precedence relations among jobs. Our objective is to minimize makespan (Maximum completion time of all machines), number of tardy jobs, total tardiness and total earliness at the same time which can be more advantageous in real environment than considering each of objectives separately. For obtaining an optimal solution, hybrid algorithm based on MOGA and SA has been proposed in order to gain both good global and local search abilities. Simulation results and four well-known multi-objective performance metrics, indicate that the proposed hybrid algorithm outperforms the genetic algorithm (GA) and SA in terms of each objective and significantly in minimizing the total cost of the weighted function.

Mathematical Models of Energy-Conscious Bi-Objective Unrelated Parallel Machine Scheduling

“…A mathematical model for UPMS under TOU has been presented by Hossein et al [21]. For research of multi-objective UPMS, Nikabadi and Naderi [22] have proposed a hybrid genetic algorithm (GA) and simulated annealing (SA) to handle the multi-objective UPMS. The objectives are the number of tardy jobs and total tardiness/earliness.…”