Research on Algorithms Performance about JSP Scheduling

Dong Wei Qiu; Shan Shan Wan

doi:10.4028/www.scientific.net/AMR.457-458.20

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 457-458Research on Algorithms Performance about JSP...

Research on Algorithms Performance about JSP Scheduling

Abstract:

Three typical intelligent evolutionary algorithms are applied on Job Shop scheduling problem which are Quantum algorithm, Genetic Algorithm and Population Based Incremental Learning algorithm. They three algorithms have some common features in computation, encoding strategy and probability application, but with the different problems and different scale sizes of the same problem they show different performance. In this paper we take JSP as example to test their performance difference and analyze their applicability. Two benchmark Job Shop problems are used to fulfill the comparison. The results denote that Quantum algorithm is good in a great quantity of solution individual, GA is excellent in stability and PBIL had good performance in accuracy. The research also makes a reliable instruction on the application or combination of the three algorithms.

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Periodical:

Advanced Materials Research (Volumes 457-458)

Pages:

20-25

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.457-458.20

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Online since:

January 2012

Authors:

Dong Wei Qiu, Shan Shan Wan

Keywords:

Genetic Algorithm (GA), PBIL, Quantum Algorithm

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