Variable Neighborhood Search Algorithms for the multi-depot dial-a-ride problem with heterogeneous vehicles and users

TL;DR

This paper develops and compares Variable Neighborhood Search algorithms for a complex multi-depot dial-a-ride problem involving heterogeneous vehicles and users, aiming to minimize routing costs while satisfying multiple constraints.

Contribution

It introduces a comprehensive VNS framework with various initial solutions, neighborhoods, and local search procedures tailored for multi-depot dial-a-ride problems with heterogeneity.

Findings

VNS algorithms effectively reduce routing costs.

The adjusting procedure improves feasibility of solutions.

Algorithms perform well on real-world healthcare instances.

Abstract

In this work, a study on Variable Neighborhood Search algorithms for multi-depot dial-a-ride problems is presented. In dial-a-ride problems patients need to be transported from pre-specified pickup locations to pre-specified delivery locations, under different considerations. The addressed problem presents several constraints and features, such as heterogeneous vehicles, distributed in different depots, and heterogeneous patients. The aim is of minimizing the total routing cost, while respecting time-window, ride-time, capacity and route duration constraints. The objective of the study is of determining the best algorithm configuration in terms of initial solution, neighborhood and local search procedures. At this aim, two different procedures for the computation of an initial solution, six different type of neighborhoods and five local search procedures, where only intra-route changes are made, have been considered and compared. We have also evaluated an "adjusting procedure" that aims to produce feasible solutions from infeasible solutions with small constraints violations. The different VNS algorithms have been tested on instances from literature as well as on random instances arising from a real-world healthcare application.