alexa Abstract | An improved genetic algorithm to the job shop scheduling problem

Journal of Chemical and Pharmaceutical Research
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For a dynamic, changeable agile manufacturing system, a dynamic job shop scheduling approach is one of effective measures for production management. In this paper, an improved genetic algorithm is proposed to the job shop scheduling problem. The experimental results suggest that this improved genetic algorithm is correct, feasible and available. The data-driven optimization method is a new approach to study the agile manufacturing system.

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Author(s): Yu YanFang and Ying Yue


Job shop scheduling, data-driven, genetic algorithm, dynamic optimization

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