Multi-objective Particle Swarm Optimisation for Robust Dynamic Scheduling in a Permutation Flow Shop

Abstract

This paper proposes a multi-objective optimisation model and particle swarm optimisation solution method for the robust dynamic scheduling of permutation flow shop in the presence of uncertainties. The proposed optimisation model for robust scheduling considers utility, stability and robustness measures to generate robust schedules that minimise the effect of different real-time events on the planned schedule. The proposed solution method is based on a predictive-reactive approach that uses particle swarm optimisation to generate robust schedules in the presence of real-time events. The evaluation of both the optimisation model and solution method are conducted considering different types of disruptions including machine breakdown and new job arrival. The obtained results showed that the proposed model and solution method gives better results than a bi-objective model that considers only utility and stability measures [1] and the classical makespan model.