A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms

• A methodology for addressing multi objective flexible job shop scheduling problem is proposed.
• Stability and makespan considered to optimize simultaneously in presence of machine breakdown.
• NRGA, NSGAII, and simulation used to tackle the problem.
• In three criteria NSGAII is leading algorithm and for two criteria NRGA is the leading one.

This paper addresses the stable scheduling of multi-objective problem in flexible job shop scheduling with random machine breakdown. Recently, numerous studies are conducted about robust scheduling; however, implementing a scheme which prevents a tremendous change between scheduling and after machine breakdown (preschedule and realized schedule, respectively) can be critical for utilizing available resources. The stability of the schedule can be detected by a slight deviation of start and completion time of each job between preschedule and realized schedule under the uncertain conditions. In this paper, two evolutionary algorithms, NSGA-II and NRGA, are applied to combine the improvement of makespan and stability simultaneously. A simulation approach is used to evaluate the state and condition of the machine breakdowns. After the introduction of the evaluation criteria, the proposed algorithms are tested on a variety of benchmark problems. Finally, through performing statistical tests, the algorithm with higher performance in each criterion is identified.