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首页> 外文期刊>Journal of advances in management research >Scheduling of a flexible job-shop using a multi-objective genetic algorithm
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Scheduling of a flexible job-shop using a multi-objective genetic algorithm

机译：使用多目标遗传算法的柔性作业车间调度

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Purpose - The purpose of this paper is to solve a flexible job shop scheduling problem where alternate machines are available to process the same job. The study considers the Flexible Job Shop Problem (FJSP) having n jobs and more than three machines for scheduling. Design/methodology/approach - FJSP for n jobs and more than three machines is non polynomial (NP) hard in nature and hence a multi-objective genetic algorithm (GA) based approach is presented for solving the scheduling problem. The two objective functions formulated are minimizations of the make-span time and total machining time. The algorithm uses a unique method of generating initial populations and application of genetic operators. Findings - The application of GA to the multi-objective scheduling problem has given optimum solutions for allocation of jobs to the machines to achieve nearly equal utilisation of machine resources. Further, the make span as well as total machining time is also minimized. Research limitations/implications - The model can be extended to include more machines and constraints such as machine breakdown, inspection etc., to make it more realistic. Originality/value - The paper presents a successful implementation of a meta-heuristic approach to solve a NP-hard problem of FJSP scheduling and can be useful to researchers and practitioners in the domain of production planning.
机译：目的-本文的目的是解决灵活的车间调度问题，在这种情况下可以使用备用机器来处理同一作业。该研究认为，柔性车间作业（FJSP）具有n个作业和多于三个用于调度的机器。设计/方法/方法-用于n个作业和多于三台机器的FJSP本质上是非多项式（NP）的，因此，提出了一种基于多目标遗传算法（GA）的方法来解决调度问题。制定的两个目标函数是最小化制造时间和总加工时间。该算法使用一种独特的方法来生成初始种群并应用遗传算子。研究结果-遗传算法在多目标调度问题上的应用为将作业分配给机器提供了最佳解决方案，以实现几乎相等的机器资源利用率。此外，制造跨度以及总加工时间也被最小化。研究的局限性/意义-该模型可以扩展为包括更多的机器和约束，例如机器故障，检查等，以使其更加真实。原创性/价值-本文提出了一种成功的元启发式方法，可以解决FJSP调度的NP难题，并且对生产计划领域的研究人员和从业人员有用。

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