DC-NDEA模型和LM-CNN驱动下的生鲜农产品供应链绩效评估与预测

doi:10.6040/j.issn.1671-9352.0.2025.077

摘要/Abstract

摘要： 目前针对冷链物流供应链绩效评估与预测的研究较少,考虑冷链物流运作中的最终产出,结合可持续发展中的循环利用和供应链跨时期运作的特点,设计一个动态循环网络数据包络分析(dynamic cyclic-network data envelopment analysis, DC-NDEA)模型进行绩效评估。发现期望产出和不期望产出的变化,对冷链物流供应链绩效影响显著,该模型考虑了不同时间段之间的相互作用,为管理者提供了审视相关因素在供应链绩效变化中的视角,为管理者进行资源分配和决策提供帮助。同时采用Levenberg-Marquard算法改进卷积神经网络(convolutional neural networks, CNN)以最小的绝对误差进行绩效预测。结果表明LM-CNN有效提升了模型预测结果的准确性,通过使用人工神经网络预测未来时间段供应链的绩效,帮助管理者在效率低下之前采取积极的措施,从而提高供应链运作的效率。

关键词: 生鲜农产品供应链, 可持续发展, 绩效评估, 绩效预测, DC-NDEA模型, LM-CNN预测模型

Abstract: In the supply chain management of fresh agricultural products, the performance of cold chain logistics supply chain is one of the important factors for the sustainable development of fresh enterprises. However, there are few researches on performance evaluation and prediction at present. Therefore, considering the final output of cold chain logistics operation, combined with the characteristics of recycling in sustainable development and cross-period operation of supply chain, A Dynamic Cyclic Network Data Envelopment Analysis model is designed for performance evaluation. It is found that the changes of expected output and unexpected output have a significant impact on the performance of cold chain logistics supply chain. The model considers the interaction between different time periods, provides a perspective for managers to examine the changes of relevant factors in supply chain performance, and provides help for managers to make resource allocation and decision. Meanwhile, Levenberg-Marquard algorithm is used to im-prove Convolutional Neural Networks to predict performance with minimum absolute error. The results show that LM-CNN effectively improves the accuracy of model prediction results. By using artificial neural network to predict the performance of supply chain in the future period, it helps managers to take active measures before the inefficiency, so as to improve the efficiency of supply chain operation.

Key words: fresh produce supply chain, sustainable development, performance evaluation, performance prediction, DV-NDEA model, LM-CNN predictive model

中图分类号:

F252

张天瑞,高凯,何权峰. DC-NDEA模型和LM-CNN驱动下的生鲜农产品供应链绩效评估与预测[J]. 《山东大学学报(理学版)》, 2026, 61(8): 16-28.

ZHANG Tianrui, GAO Kai, HE Quanfeng. Evaluation and prediction of fresh agricultural supply chain performance driven by DC-NDEA model and LM-CNN[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2026, 61(8): 16-28.

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