Paper Info
Title
Simplified multi-objective genetic algorithms for stochastic job shop scheduling
Abstract
Job shop scheduling with multi-objective has been extensively investigated; however, multi-objective stochastic job shop scheduling problem is seldom considered. In this paper, a simplified multi-objective genetic algorithm (SMGA) is proposed for the problem with exponential processing time. The objective is to minimize makespan and total tardiness ratio simultaneously. In SMGA, the chromosome of the problem is ordered operations list, an effective schedule building procedure is proposed, a novel crossover is used, and a simplified binary tournament selection and a simple external archive updating strategy are adopted. SMGA is finally tested on some benchmark problems and compared with some methods from literature. Computational results demonstrate that the good performance of SMGA on the problem.
Year
DOI
Venue
2011
10.1016/j.asoc.2011.06.001
Appl. Soft Comput.
Keywords
Field
DocType
computational result,multi-objective stochastic job shop,simplified multi-objective genetic algorithm,binary tournament selection,job shop scheduling,good performance,exponential processing time,effective schedule building procedure,benchmark problem,stochastic job shop scheduling,multi-objective genetic algorithm,scheduling problem,genetic algorithm
Mathematical optimization,Crossover,Job shop scheduling,Tardiness,Computer science,Flow shop scheduling,Rate-monotonic scheduling,Tournament selection,Genetic algorithm,Binary number
Journal
Volume
Issue
ISSN
11
8
1568-4946
Citations 
PageRank 
References 
7
0.47
4
Authors
1
Name
Order
Citations
PageRank
De-ming Lei117618.60