Solving multi-objective parallel machine scheduling problem by a modified NSGA-II

In this paper, we modify a Multi-Objective Evolutionary Algorithm, known as Nondominated sorting Genetic Algorithm-II (NSGA-II) for a parallel machine scheduling problem with three objectives. The objectives are – (1) minimization of total cost due tardiness, (2) minimization of the deterioration co...

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Vydané v:Applied mathematical modelling Ročník 37; číslo 10-11; s. 6718 - 6729
Hlavní autori: Bandyopadhyay, Susmita, Bhattacharya, Ranjan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 01.06.2013
Predmet:
Algorithms
Cost engineering
Evolutionary algorithms
Genetics
Job sequencing
Mathematical models
Minimization
Multi-Objective Evolutionary Algorithm (MOEA)
Mutations
NSGA-II (Non-dominated Sorting Genetic Algorithm-II)
Optimization
Scheduling
SPEA2
Multi-Objective Evolutionary Algorithm (MOEA)
Job sequencing
NSGA-II (Non-dominated Sorting Genetic Algorithm-II)
SPEA2
ISSN:0307-904X
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Shrnutí:In this paper, we modify a Multi-Objective Evolutionary Algorithm, known as Nondominated sorting Genetic Algorithm-II (NSGA-II) for a parallel machine scheduling problem with three objectives. The objectives are – (1) minimization of total cost due tardiness, (2) minimization of the deterioration cost and (3) minimization of makespan. The formulated problem has been solved by three Multi-Objective Evolutionary Algorithms which are: (1) the original NSGA-II (Non-dominated Sorting Genetic Algorithm–II), (2) SPEA2 (Strength Pareto Evolutionary Algorithm-2) and (3) a modified version of NSGA-II as proposed in this paper. A new mutation algorithm has also been proposed depending on the type of problem and embedded in the modified NSGA-II. The results of the three algorithms have been compared and conclusions have been drawn. The modified NSGA-II is observed to perform better than the original NSGA-II. Besides, the proposed mutation algorithm also works effectively, as evident from the experimental results.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0307-904X
DOI:10.1016/j.apm.2013.01.050