A hybrid metaheuristic for solving asymmetric distance-constrained vehicle routing problem

Ha-Bang Ban,Phuong Khanh Nguyen

doi:10.1186/s40649-020-00084-7

Ha-Bang Ban, Phuong Khanh Nguyen

Open Access

https://doi.org/10.1186/s40649-020-00084-7

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Abstract

The Asymmetric Distance-Constrained Vehicle Routing Problem (ADVRP) is NP-hard as it is a natural extension of the NP-hard Vehicle Routing Problem. In ADVRP problem, each customer is visited exactly once by a vehicle; every tour starts and ends at a depot; and the traveled distance by each vehicle is not allowed to exceed a predetermined limit. We propose a hybrid metaheuristic algorithm combining the Randomized Variable Neighborhood Search (RVNS) and the Tabu Search (TS) to solve the problem. The combination of multiple neighborhoods and tabu mechanism is used for their capacity to escape local optima while exploring the solution space. Furthermore, the intensification and diversification phases are also included to deliver optimized and diversified solutions. Extensive numerical experiments and comparisons with all the state-of-the-art algorithms show that the proposed method is highly competitive in terms of solution quality and computation time, providing new best solutions for a number of instances.

Highlights

Introduction and literature review The basicVehicle Routing Problem (VRP) aims to design the least cost routes from a single depot to a set of geographically distributed customers such that each customer is visited exactly once by one vehicle
We study in this paper a variant of VRP named Distance-Constrained VRP (DVRP)
Each solution T produced by the Randomized Variable Neighborhood Search (RVNS)+Tabu Search (TS) in Step 2 of Algorithm 1 is inserted into the set P if its fitness is not worse more than 10% to that of the current best solution T ∗

Summary

Introduction

Introduction and literature review The basicVehicle Routing Problem (VRP) aims to design the least cost routes from a single depot to a set of geographically distributed customers such that each customer is visited exactly once by one vehicle. Each solution T produced by the RVNS+TS in Step 2 of Algorithm 1 is inserted into the set P if its fitness is not worse more than 10% to that of the current best solution T ∗ . The performance of the proposed algorithm is evaluated through comparison with published results on the instances provided in the literature of ADVRP, ACVRP, MTRPD.

Results

Conclusion