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Improved Genetic Algorithm for Extension Dual Resource Constrained Job Shop Scheduling Problem

In: Liss 2014

Author

Listed:
  • Jingyao Li

    (Northwestern Polytechnical University)

  • Yuan Huang

    (Northwestern Polytechnical University)

Abstract

In this paper a mathematical model was built for extension dual resource constrained job shop scheduling problem which takes into account the specific characteristics of numerical control devices, and a branch population genetic algorithm was constructed on the basis of inheriting evolution experience of parent chromosome population with pheromone. In addition this algorithm used some optimization operators to optimize algorithm performance, such as the elite evolutionary operator, the Pareto solution rapid selection operator based on the dominated area, the roulette selection operator based on sector partition, and so on. At last the statistical analysis on the simulation results of strategies comparison simulation, algorithm performance comparison simulation and real case calculation simulation proved that these optimization mechanisms could effectively improve the calculation efficiency and optimization effect of the algorithm.

Suggested Citation

  • Jingyao Li & Yuan Huang, 2015. "Improved Genetic Algorithm for Extension Dual Resource Constrained Job Shop Scheduling Problem," Springer Books, in: Zhenji Zhang & Zuojun Max Shen & Juliang Zhang & Runtong Zhang (ed.), Liss 2014, edition 127, pages 1105-1110, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-43871-8_159
    DOI: 10.1007/978-3-662-43871-8_159
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