DocumentCode
2912012
Title
Multi-objective DE and PSO strategies for production scheduling
Author
Grobler, Jacomine ; Engelbrecht, Andries P. ; Yadavalli, V.S.S.
Author_Institution
Dept. of Ind. & Syst. Eng., Univ. of Pretoria, Tshwane
fYear
2008
fDate
1-6 June 2008
Firstpage
1154
Lastpage
1161
Abstract
This paper investigates the application of alternative multi-objective optimization (MOO) strategies to a complex scheduling problem. Two vector evaluated algorithms, namely the vector evaluated particle swarm optimization (VEPSO) algorithm as well as the vector evaluated differential evolution (VEDE) algorithm is compared to a differential evolution based modified goal programming approach. This paper is considered significant since no other reference to the application of vector evaluated algorithms in a scheduling environment could be found. Algorithm performance is evaluated on real customer data and meaningful conclusions are drawn with respect to the application of MOO algorithms in a multiple machine multi-objective scheduling environment.
Keywords
evolutionary computation; particle swarm optimisation; production control; scheduling; vectors; multiobjective optimization strategies; multiple machine multiobjective scheduling environment; production scheduling; vector evaluated differential evolution algorithm; vector evaluated particle swarm optimization algorithm; Africa; Customer satisfaction; Decision making; Genetic programming; Job shop scheduling; Manufacturing; Particle swarm optimization; Processor scheduling; Production; Scheduling algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-1822-0
Electronic_ISBN
978-1-4244-1823-7
Type
conf
DOI
10.1109/CEC.2008.4630942
Filename
4630942
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