Deep reinforcement learning for solving the heterogeneous capacitated vehicle routing problem

Publication Type

Journal Article

Publication Date

9-2021

Abstract

Existing deep reinforcement learning (DRL)-based methods for solving the capacitated vehicle routing problem (CVRP) intrinsically cope with a 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 article, 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, that is, slack induction by string removal. In addition, the results of extended experiments demonstrate that our method is also able to solve CVRPLib instances with satisfactory performance.

Keywords

Decoding, Search problems, Reinforcement learning, Computer architecture, Vehicle routing, Routing, Optimization, Deep reinforcement learning (DRL), heterogeneous CVRP (HCVRP), min-max objective, min-sum objective

Discipline

Management Information Systems

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Cybernetics

Volume

52

Issue

12

First Page

13572

Last Page

13585

ISSN

2168-2267

Identifier

10.1109/TCYB.2021.3111082

Publisher

Institute of Electrical and Electronics Engineers

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