Ant colony optimization, genetic algorithm and hybrid metaheuristics: A new solution for parallel machines scheduling with sequence-dependent set-up times

Abstract

The parallel machine scheduling problem (PMSP) is one of the most difficult classes of problem. Due to the complexity of the problem, obtaining optimal solution for the problems with large size is very time consuming and sometimes, computationally infeasible. So, heuristic algorithms that provide near-optimal solutions are more practical and useful. The present study aims to propose a hybrid metaheuristic approach for solving the problem of unrelated parallel machine scheduling, in which, the machine and the job sequence dependent setup times are considered. A Mixed-Integer Programming (MIP) model is formulated for the unrelated PMSP with sequence dependent setup times. The solution approach is robust, fast, and simply structured. The hybridization of Genetic Algorithm (GA) with Ant Colony Optimization (ACO) algorithm is the key innovative aspect of the approach. This hybridization is made in order to accelerate the search process to near-optimal solution. After computational and statistical analysis, the two proposed algorithms are used to compare with the proposed hybrid algorithm to highlight its advantages in terms of generality and quality for short and large instances. The results show that the proposed hybrid algorithm has a very good performance as regards the instance size and provides the acceptable results.