Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem

TL;DR

This paper introduces a deep reinforcement learning approach tailored for heterogeneous vehicle routing problems, effectively handling diverse vehicle capacities and outperforming existing methods in various scenarios.

Contribution

It proposes a novel DRL model with attention mechanisms that considers vehicle heterogeneity, enabling better solutions for complex CVRP variants.

Findings

Outperforms state-of-the-art DRL methods and heuristics on synthetic instances.

Demonstrates strong generalization to different problem sizes.

Achieves competitive results on benchmark CVRPLib instances.

Abstract

Existing deep reinforcement learning (DRL) based methods for solving the capacitated vehicle routing problem (CVRP) intrinsically cope with homogeneous vehicle fleet, in which the fleet is assumed as repetitions of a single vehicle. Hence, their key to construct a solution solely lies in the selection of the next node (customer) to visit excluding the selection of vehicle. However, vehicles in real-world scenarios are likely to be heterogeneous with different characteristics that affect their capacity (or travel speed), rendering existing DRL methods less effective. In this paper, we tackle heterogeneous CVRP (HCVRP), where vehicles are mainly characterized by different capacities. We consider both min-max and min-sum objectives for HCVRP, which aim to minimize the longest or total travel time of the vehicle(s) in the fleet. To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step. Experimental results based on randomly generated instances show that, with desirable generalization to various problem sizes, our method outperforms the state-of-the-art DRL method and most of the conventional heuristics, and also delivers competitive performance against the state-of-the-art heuristic method, i.e., SISR. Additionally, the results of extended experiments demonstrate that our method is also able to solve CVRPLib instances with satisfactory performance.