Natural Computing

, Volume 13, Issue 2, pp 145–156 | Cite as

Robust swarm optimisation for fuzzy open shop scheduling

  • Juan José Palacios
  • Inés González-Rodríguez
  • Camino R. Vela
  • Jorge Puente
Article

Abstract

In this paper we consider a variant of the open shop problem where task durations are allowed to be uncertain and where uncertainty is modelled using fuzzy numbers. Solutions to this problem are fuzzy schedules, which we argue should be seen as predictive schedules, thus establishing links with the concept of robustness and a measure thereof. We propose a particle swarm optimization (PSO) approach to minimise the schedule’s expected makespan, using priorities to represent particle position, as well as a decoding algorithm to generate schedules in a subset of possibly active ones. Our proposal is evaluated on a varied set of several benchmark problems. The experimental study includes a parametric analysis, results of the PSO compared with the state-of-the-art, and an empirical study of the robustness of taking into account uncertainty along the scheduling process.

Keywords

Open shop scheduling Fuzzy durations Particle swarm optimisation Robustness 

Supplementary material

11047_2014_9413_MOESM1_ESM.pdf (301 kb)
PDF (301 KB)

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Juan José Palacios
    • 1
  • Inés González-Rodríguez
    • 2
  • Camino R. Vela
    • 1
  • Jorge Puente
    • 1
  1. 1.Department of Computer ScienceUniversity of OviedoOviedoSpain
  2. 2.Department of Mathematics, Statistics and ComputingUniversity of CantabriaSantanderSpain

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