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Effective selection and allocation of material handling equipment for stochastic production material demand problems using genetic algorithm

机译:使用遗传算法有效选择和分配用于随机生产物料需求问题的物料搬运设备

摘要

This paper addresses the stochastic production demand problem in a manufacturing company. The objective of this research is to minimize the waiting time of production workstations and reduce stochastic production material problems through coordinating pickup and delivery orders in a warehouse. RFID technology is adopted to visualize the actual status of operations in production and warehouse environments. A mathematical model is developed to address this problem and a meta-heuristic algorithm using genetic algorithm (GA) is also developed to improve performance. Computational experiments are undertaken to examine the performance of the algorithm when dealing with congestion in cases of heavy and normal demand for production material. The overall result shows that the algorithm efficiently minimizes the total makespan of the production shop floor.
机译:本文解决了制造公司中的随机生产需求问题。这项研究的目的是通过协调仓库中的提货和交货订单来最大程度地减少生产工作站的等待时间,并减少随机生产材料的问题。采用RFID技术来可视化生产和仓库环境中的实际运行状态。开发了数学模型来解决此问题,并且还开发了使用遗传算法(GA)的元启发式算法来提高性能。在对生产材料有大量需求和正常需求的情况下,进行计算实验以检查算法在处理拥塞时的性能。总体结果表明,该算法有效地最小化了生产车间的总工期。

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