随机人工智能效益与供应链管理库存预测

Naima El Haoud, Zineb Bachiri
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引用次数: 5

摘要

供应链管理(SCM)包括几个复杂的过程,每个过程对于有效的供应链的维护同样重要。供应链是复杂的系统,其中合作伙伴的行动和协调影响整个系统的性能。提高竞争力和对客户快速反应的需要需要使用有效的管理技术。传统上,启发式或数学规划技术已用于供应链管理。单品分析是供应链中常用的优化方法。这忽略了不同实体之间存在动态交互以及优化必须作为一个整体进行的事实。随机模型和人工智能在供应链管理中的应用有限。为了利用随机内部分析对供应链管理的潜在好处,我们在本文中提出了我们的贡献,即将CEW和随机方法相结合,以帮助解决预测中的实际问题。的股票。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stochastic Artificial Intelligence benefits and Supply Chain Management inventory prediction
Supply chain management (SCM) includes several complex processes, each process being equally important for the maintenance of an efficient supply chain. Supply chains are complex systems where partner actions and coordination affect the performance of the system as a whole. Increasing competitiveness and the need for rapid customer responses require the use of effective management techniques. Traditionally, heuristic or mathematical programming techniques have been used in SCM. Individual item analysis is a common optimization method in supply chains. This ignores the fact that there are dynamic interactions between different entities and that the optimization must be done as a whole. Stochastic models and AI AI have seen limited application in Supply Chain Management (SCM). In order to exploit the potential benefits of stochastic IA for supply chain management, we present in this paper our contribution as a combination of CEW and stochastic approaches to help solve practical problems in forecasting. of stock.
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