Dynamic Inventory Routing Optimization Based on Deep Reinforcement Learning

Authors

Jianpin Zhou, 1. School of Navigation, Jimei University, Xiamen 361021, China; ;
Shuliu Zhang, 2. Jilin Power Supply Company of State Grid, Jilin 132000, China;

Abstract

Abstract: Aiming at the dynamic stochastic inventory routing problem with periodic fluctuation of demand, a novel simulation optimization approach based on deep reinforcement learning is proposed to achieving periodic steady strategy. Firstly a dynamic combinatorial optimization model is constructed. Then, by deep reinforcement learning and setting heuristic rules, the replenishment nodes set selection and the replenishment batch allocation weights in each period are determined. The simulation experimental results show that the proposed method can improve the average profit of a cycle by about 2.7% and 3.9% in low fluctuating demand case and by about 8.2% and 7.1% in high fluctuating demand case compared with the two solution methods in the existing literature, and the cycle service level can be stabilized within a small fluctuation range under different demand fluctuation environments.

Recommended Citation

Zhou, Jianpin and Zhang, Shuliu (2019) "Dynamic Inventory Routing Optimization Based on Deep Reinforcement Learning," Journal of System Simulation: Vol. 31: Iss. 10, Article 22.
DOI: 10.16182/j.issn1004731x.joss.18-0820
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss10/22

First Page

2155

Revised Date

2019-02-15

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.18-0820

Last Page

2163

CLC

TP391.9

Recommended Citation

Zhou Jianpin, Zhang Shuliu. Dynamic Inventory Routing Optimization Based on Deep Reinforcement Learning[J]. Journal of System Simulation, 2019, 31(10): 2155-2163.

DOI

10.16182/j.issn1004731x.joss.18-0820