An Adaptive Iterated Local Search Heuristic for the Heterogeneous Fleet Vehicle Routing Problem

TL;DR

This paper introduces an Adaptive Iterated Local Search heuristic tailored for the Heterogeneous Fleet Vehicle Routing Problem, effectively reducing costs in complex routing scenarios with diverse vehicle types.

Contribution

The paper develops a novel adaptive meta-heuristic specifically designed for HFVRP, demonstrating superior performance over existing methods on benchmark datasets.

Findings

Outperformed state-of-the-art metaheuristics on 87% of benchmark instances.

Effective handling of large instances with up to 360 customers.

Adaptive behavior improves solution diversity and quality.

Abstract

The Heterogeneous Fleet Vehicle Routing Problem (HFVRP) is an important variant of the classical Capacitated Vehicle Routing Problem (CVRP) that aims to find routes that minimize the total traveling cost of a heterogeneous fleet of vehicles. This problem is of great interest given its importance in many industrial and commercial applications. In this paper, we present an Adaptive Iterated Local Search (AILS) heuristic for the HFVRP. AILS is a local search-based meta-heuristic that achieved good results for the CVRP. The main characteristic of AILS is its adaptive behavior that allows the adjustment of the diversity control of the solutions explored during the search process. The proposed AILS for the HFVRP was tested on benchmark instances containing up to 360 customers. The results of computational experiments indicate that AILS outperformed state-of-the-art metaheuristics on 87\% of the instances.