Solving a multi-objective open shop scheduling problem by a novel hybrid ant colony optimization

This paper considers an open shop scheduling problem that minimizes bi-objectives, namely makespan and total tardiness. This problem, due to its complexity, is ranked in the class of NP-hard problems. In this case, traditional approaches cannot reach to an optimal solution in a reasonable time. Thus, we propose an efficient method based on multi-objective simulated annealing and ant colony optimization, in order to solve the given problem. Furthermore a decoding operator is applied in order to improve the quality of generated schedules. Finally, we compare our computational results with a well-known multi-objective genetic algorithm, namely NSGA II. In addition, comparisons are made in single objective case. The outputs show encouraging results in the form of the solution quality.