Enhanced evolutionary algorithms for single and multiobjective optimization in the job shop scheduling problem

Over the past few years, a continually increasing number of research efforts have investigated the application of evolutionary computation techniques for the solution of scheduling problems. Scheduling can pose extremely complex combinatorial optimization problems, which belong to the NP-hard family...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 15; no. 1; pp. 13 - 25
Main Authors: Esquivel, S., Ferrero, S., Gallard, R., Salto, C., Alfonso, H., Schütz, M.
Format: Journal Article
Language:English
Published: Elsevier B.V 2002
Subjects:
Artificial intelligence
Evolutionary algorithms
Genetic algorithms
Job shop scheduling
Multiple object problems
Multiplicity of crosoovers and parents
Scheduling
Single and multiobjective optimization
Multiplicity of crosoovers and parents
Scheduling
Evolutionary algorithms
Single and multiobjective optimization
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:Over the past few years, a continually increasing number of research efforts have investigated the application of evolutionary computation techniques for the solution of scheduling problems. Scheduling can pose extremely complex combinatorial optimization problems, which belong to the NP-hard family. Last enhancements on evolutionary algorithms include new multirecombinative approaches. Multiple Crossovers Per Couple (MCPC) allows multiple crossovers on the couple selected for mating and Multiple Crossovers on Multiple Parents (MCMP) do this but on a set of more than two parents. Techniques for preventing incest also help to avoid premature convergence. Issues on representation and operators influence efficiency and efficacy of the algorithm. The present paper shows how enhanced evolutionary approaches, can solve the Job Shop Scheduling Problem (JSSP) in single and multiobjective optimization.
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ISSN:0950-7051
1872-7409
DOI:10.1016/S0950-7051(01)00117-4