Volume 5 Number 3 (Mar. 2010)
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JSW 2010 Vol.5(3): 269-274 ISSN: 1796-217X
doi: 10.4304/jsw.5.3.269-274

A Genetic Algorithm for Job-Shop Scheduling

Ye Li, Yan Chen
Transportation Management College, Dalian Maritime University, Dalian, PR.China

Abstract—In this paper, we analyze the characteristics of the job shop scheduling problem. A new genetic algorithm for solving the agile job shop scheduling is presented to solve the job shop scheduling problem. Initial population is generated randomly. Two-row chromosome structure is adopted based on working procedure and machine distribution. The relevant crossover and mutation operation is also designed. It jumped from the local optimal solution, and the search area of solution is improved. Finally, the algorithm is tested on instances of 8 working procedure and 5 machines. The result shows that the genetic algorithm has been successfully applied to the job shop scheduling problems efficiency.

Index Terms—job shop scheduling, genetic algorithm, initial population, crossover and mutation operation.

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Cite: Ye Li, Yan Chen, "A Genetic Algorithm for Job-Shop Scheduling," Journal of Software vol. 5, no. 3, pp. 269-274, 2010.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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