Dynamic scheduling in flexible job shop systems by considering simultaneously efficiency and stability

Abstract

Scheduling for the flexible job shop is very important in the fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem in medium and actual size problems with traditional optimization approaches owing to the high computational complexity. In this paper, dynamic scheduling in flexible job shop is considered. The dynamic status intensifies the complexity of this problem. Nevertheless, there are many industries which have a dynamic status. Two objectives are considered to make a balance between efficiency and stability of the schedules. A multi-objective mathematical model for the considered problem is developed. Since the problem is well known as NP-hard, a meta-heuristic algorithm based on the genetic algorithm is developed. Numerical experiments are used to evaluate the performance and efficiency of the proposed algorithm. The experimental results show that the proposed algorithm is capable to achieve the optimal solutions for the small size problems and near optimal solutions for the medium size problems.