Applying an Intelligent Dynamic Genetic Algorithm for Solving a Multi-Objective Flexible Job Shop Scheduling Problem with Maintenance Considerations

Abstract

In this paper, a multi-objective flexible dynamic job shop scheduling problem (MO-FDJSPM) with maintenance constraint is studied. The objectives of the scheduling are maximizing the completion time, mean job rotation time and mean components' tardiness. Also, in order to adapt with the internal disruptions of the manufacturing system, such as breakdown of existing machines, we consider the machines availability (so called maintenance) as a constraint. The multi-objective mathematical model is formulated and a genetic algorithm (GA) with dynamic bidimensional chromosomes along with a heuristic algorithm to handle maintenance sub-problem is developed as solution approach. In proposed algorithm, since the control parameters change intelligently and dynamically during implementation and optimization process, the early convergence and trapping in local optimum are reduced leading to performance improvement. The performance of the proposed approach is evaluated with respect to convergence speed and solutions quality. The results of computations verify and confirm both two evaluation criteria.