利用递归神经网络研究易腐产品仓库的双源库存系统

Fangyu Sun
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摘要

长期以来,学术界一直在寻找从多个供应商补充库存的最佳策略。为了解决这些优化问题,库存管理者需要决定在净库存和未完成订单的情况下,向每个供应商订购多少库存,以便使预期的积压、持有和采购成本共同最小化。学者们对这一问题的研究由来已久,需要考虑的因素很多,如如何使采购成本最小化等。本文从神经网络的角度出发,将动态库存和动态需求纳入递归神经网络的设计中。结果表明,利用深度神经网络优化方法可以获得高质量的解决方案,为有效管理复杂的高维库存动态开辟了一条新途径
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Research on the dual source inventory system of perishable product warehouse using recurrent neural networks
For a long time, the academic community has been searching for the best strategy to replenish inventory from multiple suppliers. To address these optimization problems,inventory managers need to decide how much to order from each vendor in the case of net inventory and outstanding orders in order to minimize the expected backlog,holding and procurementocsts jointly.Especially in terms of perishable products, there are many factors to consider. Scholars have been studying this issue for a long time, and there are many factors that need to be considered, such as how to minimize procurement costs. This article incorporates dynamic inventory and dynamic demand into the design of recurrent neural networks from the perspective of neural networks. The results indicate that using deep neural network optimization methods can obtain high-quality solutions and open up a new approach for effective management of complex high-dimensional inventory dynamics
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