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

Jingyao Li () and Yuan Huang ()
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Jingyao Li: Northwestern Polytechnical University
Yuan Huang: Northwestern Polytechnical University

A chapter in LISS 2014, 2015, pp 1105-1110 from Springer

Abstract: 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.

Keywords: Dual resource constrained; Branch population; Elite evolutionary; Dominated area; Sector partition (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-662-43871-8_159

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DOI: 10.1007/978-3-662-43871-8_159

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