基于DEA-Malmquist算法的原油贸易采购模型研究

Sci. Program. Pub Date : 2021-12-27 DOI:10.1155/2021/6360439
Liu Yan
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引用次数: 9

摘要

为了最大限度地保留有价值的信息,增强对原油交易购买价格需求的挖掘能力,本文提出了一种基于DEA-Malmquist算法的原油交易购买模型。管控平台的内部网应共享同一数据库,内部网只允许管理人员访问和管理系统,只允许所有注册用户访问,并通过二维码扫描实现内部网与内部网之间的数据交换;此外,由于内部网与原油贸易采购内部网之间的资源共享,供应商和其他注册用户可以立即掌握企业的采购动态。在DEA-Malmquist算法下,运用模糊理论分析了采购管理的不确定性,建立了带有模糊参数的精细化采购决策模型。通过符号距离和重心法确定最优订货时间和采购数量。实验结果表明,该方法能有效保留初始序列中有价值的信息,对物资采购需求智能挖掘具有较好的实际应用价值。该模型获得了98.62%的最高准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Crude Oil Trade Procurement Model Based on DEA-Malmquist Algorithm
To retain valuable information to the maximum extent and enhance the ability to mine the crude oil trade purchase price demand, this paper proposes a crude oil trade purchase model based on the DEA-Malmquist algorithm. The intranet of the management and control platform shall share the same database, and the intranet shall only allow managers to access and manage the system and only allow all registered users to access and realize data exchange between the intranet and the intranet through two-dimensional code scanning; moreover, due to the resource sharing between the intranet and the intranet for crude oil trade procurement, suppliers and other registered users can immediately grasp the procurement trends of enterprises. Under the DEA-Malmquist algorithm, the uncertainty of procurement management is analyzed by fuzzy theory, and the refined procurement decision model with fuzzy parameters is established. The optimal order time and purchase quantity are determined through the symbol distance and the method of the center of gravity. Experimental results show that the method can effectively retain valuable information in the initial sequence and has better practical application value of material procurement demand intelligent mining. The proposed model obtained the highest accuracy of 98.62%.
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