Result: Multi-objective flow shop scheduling using differential evolution

Title:
Multi-objective flow shop scheduling using differential evolution
Authors:
BIN QIAN, LING WANG, HUANG, De-Xian, XIONG WANG
Source:
Intelligent computing in signal processing and pattern recognition (International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16-19, 2006)0ICIC 2006. :1125-1136
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 22 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Documentation, Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Algorithme génétique, Genetic algorithm, Algoritmo genético, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Atelier monogamme, Flow shop, Base connaissance, Knowledge base, Base conocimiento, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Optimisation combinatoire, Combinatorial optimization, Optimización combinatoria, Ordonnancement, Scheduling, Reglamento, Permutation, Permutación, Problème NP difficile, NP hard problem, Problema NP duro, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Recherche locale, Local search, Busca local, Robustesse, Robustness, Robustez, Système expert, Expert system, Sistema experto
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Dept. of Automation, Tsinghua Uinv Beijing, 100084, China
ISSN:
0170-8643
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18315962
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18315962
Database:
PASCAL Archive

Further Information

This paper proposes an effective Differential Evolution (DE) based hybrid algorithm for Multi-objective Permutation Flow Shop Scheduling Problem (MPFSSP), which is a typical NP-hard combinatorial optimization problem. In the proposed Multi-objective Hybrid DE (MOHDE), both DE-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. Firstly, to make DE suitable for solving MPFSSP, a largest-order-value (LOV) rule based on random key representation is developed to convert the continuous values of individuals in DE to job permutations. Then, to enrich the searching behaviors and to avoid premature convergence, a Variable Neighborhood Search (VNS) based local search with multiple different neighborhoods is designed and incorporated into the MOHDE. Simulation results and comparisons with the famous random-weight genetic algorithm (RWGA) demonstrate the effectiveness and robustness of our proposed MOHDE.