DOAJ Open Access 2025

Enhanced multi-task deep reinforcement learning for the integrated inventory-routing problem under VMI mode

Gang Lu Junmin Wan Lijing Du Xiaofang Chen

Abstrak

Abstract Effective inventory replenishment and routing are crucial for minimizing supply chain costs and enhancing operational efficiency. In this paper, we focus on the integrated optimization of inventory replenishment and routing problems in Vendor Managed Inventory (VMI) mode and further propose an enhanced Multi-Task Proximal Policy Optimization (MTPPO) with deep reinforcement learning. The proposed model refines inventory replenishment strategies by learning from inventory status and retailer location data. Routing strategies are optimized by utilizing a Graph Isomorphism Network (GIN) to analyze the network data of retailers and formulate routing strategies based on delivery requirements and retailer network information. By jointly optimizing inventory and routing problems, the total cost is reduced. Further, experimental results demonstrate that the MTPPO outperforms heuristic algorithms, reducing inventory costs by 8.58% and total costs by 6.18%.

Topik & Kata Kunci

Management information systems

Penulis (4)

Gang Lu

Junmin Wan

Lijing Du

Xiaofang Chen

Format Sitasi

APA MLA BibTeX

Lu, G., Wan, J., Du, L., Chen, X. (2025). Enhanced multi-task deep reinforcement learning for the integrated inventory-routing problem under VMI mode. https://doi.org/10.1007/s44176-025-00053-2

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Informasi Jurnal

Tahun Terbit: 2025
Sumber Database: DOAJ
DOI: 10.1007/s44176-025-00053-2
Akses: Open Access ✓