Multi-objective particle swarm optimisation for robust dynamic scheduling in a permutation flow shop

Mohanad Al-Behadili*, Djamila Ouelhadj, Dylan Jones

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 and the classical makespan model.
Original languageEnglish
Title of host publicationIntelligent Systems Design and Applications
Subtitle of host publication16th International Conference on Intelligent Systems Design and Applications (ISDA 2016) held in Porto, Portugal, December 16-18, 2016
EditorsA. M. Madureira, A. Abraham, D. Gamboa, P. Novais
Number of pages10
ISBN (Electronic)978-3319534800
ISBN (Print)978-3319534794
Publication statusPublished - 23 Mar 2017
Event16th International Conference on Intelligent Systems and Applications - Porto, Portugal
Duration: 16 Dec 201618 Dec 2016

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Conference16th International Conference on Intelligent Systems and Applications
Abbreviated titleISDA 2016


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