A simheuristic approach using the NSGA-II to solve a bi-objective stochastic flexible job shop problem

Abstract

This paper addresses a bi-objective problem in flexible job shop scheduling (FJSS) with stochastic processing times. Following the Just-In-Time philosophy, the first objective is to minimise deterministic Earliness+Tardiness, and the second objective is to minimise the Earliness+Tardiness Risk. The second objective function seeks to obtain robust solutions under uncertain environments. The proposed approach is a simheuristic that hybridises the non-dominated sorting genetic algorithm (NSGA-II) with Monte Carlo simulation to obtain the Pareto frontier of both objectives. The computational results demonstrate the effectiveness of the proposed algorithm under different variability environments.