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Hidden Markov Model Classifier for the Adaptive Particle Swarm Optimization Read chapter Read first chapter

2018 | OriginalPaper | Chapter

24. Surrogate-Assisted Multiobjective Evolutionary Algorithm for Fuzzy Job Shop Problems

Authors : Juan José Palacios, Jorge Puente, Camino R. Vela, Inés González-Rodríguez, El-Ghazali Talbi

Published in: Recent Developments in Metaheuristics

Publisher: Springer International Publishing

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Abstract

We consider a job shop scheduling problem with uncertain processing times modelled as triangular fuzzy numbers and propose a multiobjective surrogate-assisted evolutionary algorithm to optimise not only the schedule’s fuzzy makespan but also the robustness of schedules with respect to different perturbations in the durations. The surrogate model is defined to avoid evaluating the robustness measure for some individuals and estimate it instead based on the robustness values of neighbouring individuals, where neighbour proximity is evaluated based on the similarity of fuzzy makespan values. The experimental results show that by using fitness estimation, it is possible to reach good fitness levels much faster than if all individuals are evaluated.

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Metadata
Title
Surrogate-Assisted Multiobjective Evolutionary Algorithm for Fuzzy Job Shop Problems
Authors
Juan José Palacios
Jorge Puente
Camino R. Vela
Inés González-Rodríguez
El-Ghazali Talbi
Copyright Year
2018
Book
Recent Developments in Metaheuristics

Print ISBN: 978-3-319-58252-8

Electronic ISBN: 978-3-319-58253-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-58253-5_24