Evaluate Multi-Objective Optimization Model for Product Supply Chain Inventory Control Based on Grey Wolf Algorithm

被引：0

作者：

Zhang, Xuan ^{[1
]}

Li, Yinan ^{[1
]}

Fortes, Dilma Janete ^{[1
,2
]}

Mour, Anabela ^{[3
]}

机构：

[1] Southeast Univ, Sch Arts, Nanjing, Peoples R China

[2] Polytech Inst Viana do Castelo, Viana Do Castelo, Portugal

[3] Higher Sch Educ, Viana do Castelo, Portugal

来源：

JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING | 2024年 / 36卷 / 01期

关键词：

Deep Learning; Enterprise Decision Optimization; Grey Wolf Algorithm; Inventory Control; Multi-Objective Optimization; Product Supply Chain Evaluate;

D O I：

10.4018/JOEUC.360350

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Enterprise supply chain inventory control plays a critical role in modern enterprise management. Yet achieving multi-objective optimization remains challenging. This paper proposes a deep learning integrated model based on grey wolf optimization (GWO-DLIM) aimed at optimizing inventory holding costs, stockout costs, and order costs. The model integrates multi-layer perceptron (MLP), DeBERTa, and GWO, leveraging the powerful predictive and feature extraction capabilities of deep learning models, combined with the global optimization ability of the grey wolf algorithm, to achieve multi-objective optimization. These results demonstrate that the GWO-DLIM model significantly outperforms other comparative models across all metrics, exhibiting lower prediction errors and root mean square errors, as well as higher service levels. This study provides an effective multi-objective optimization solution for supply chain inventory management, holding significant theoretical and practical implications.

引用

页数：24

共 34 条

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