A Monte Carlo based method to maximize the service level on the makespan in the stochastic flexible job-shop scheduling problem

Abstract

This paper considers the flexible job-shop scheduling problem with stochastic processing times. Contrary to the classical criteria in the literature used to deal with random parameters, the probability of the makespan to be smaller than a predefined value, called makespan service level, is maximized. A tabu search approach combined with a Monte Carlo sampling method is proposed. Computational experiments are conducted on extended versions of benchmark instances, including the probabilistic description of random parameters. The numerical results illustrate the impact of key characteristics of the proposed approach.