Robust model for Job Shop scheduling with uncertain processing times

Abstract

This paper discusses the Job Shop scheduling problem (JSSP) with uncertain processing times to minimize the makespan. The scenario planning approach is used to represent the uncertain processing times. A robustness measure is formulated to reflect the decision-maker's preference of risk aversion, and based on this robustness measure a robust scheduling model combining the expected makespan and the robustness measure is established in this paper. The robust model can prevent the risk of deteriorating performances in bad scenarios while keeping the expected performance with little sacrifice. A genetic simulated annealing algorithm is applied to solve the robust JSSP. The performances for the robust model were compared with those for two existing models. The computational results show that the robust model presented in this paper can compromise the expected performance and the robustness, and it is advantageous to the existing models.