Robust scheduling for flexible job-shop problems with uncertain processing times

In reality, several types of uncertainties should be considered for production scheduling, and robust scheduling is a method that enable uncertainty to be taken into account. In this paper, an enhanced technique of robust scheduling in manufacturing system is proposed to handle uncertain processing times factor. Effectiveness of the proposed technique is evaluated through a case study of Flexible Job-Shop scheduling problem (FJSP) with uncertain job processing time. This paper proposes a robust scheduling method of FJSP which consists of hybridized Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) named HGABPSO. It utilizes scenarios of routing and sequences to find schedules that are confident and less sensitive against processing time uncertainties. A bi-objective evaluation measure of robust schedule is defined as minimizing the expected makespan under possible scenarios and also minimizing variability of it. Computational results indicated that the proposed method produces better solutions in comparison with a conventional method regarding the measure of robustness under different problem sizes and different levels of uncertainty for job processing time.