Vehicle Routing with Finite Time Horizon using Deep Reinforcement Learning with Improved Network Embedding

TL;DR

This paper introduces a deep reinforcement learning approach with an improved network embedding for vehicle routing within a finite time horizon, enhancing service rate and reducing solution time.

Contribution

It proposes a novel network embedding module that incorporates finite time horizon context into deep reinforcement learning for vehicle routing.

Findings

Higher customer service rate compared to existing methods

Significantly lower solution time

Effective on real-world and synthetic networks

Abstract

In this paper, we study the vehicle routing problem with a finite time horizon. In this routing problem, the objective is to maximize the number of customer requests served within a finite time horizon. We present a novel routing network embedding module which creates local node embedding vectors and a context-aware global graph representation. The proposed Markov decision process for the vehicle routing problem incorporates the node features, the network adjacency matrix and the edge features as components of the state space. We incorporate the remaining finite time horizon into the network embedding module to provide a proper routing context to the embedding module. We integrate our embedding module with a policy gradient-based deep Reinforcement Learning framework to solve the vehicle routing problem with finite time horizon. We trained and validated our proposed routing method on real-world routing networks, as well as synthetically generated Euclidean networks. Our experimental results show that our method achieves a higher customer service rate than the existing routing methods. Additionally, the solution time of our method is significantly lower than that of the existing methods.