A Hybrid Adaptive Large Neighborhood Heuristic for a Real-Life Dial-a-Ride Problem

Abstract

The transportation of elderly and impaired people is commonly solved as a Dial-A-Ride Problem (DARP). The DARP aims to design pick-up and delivery vehicle routing schedules. Its main objective is to accommodate as many users as possible with a minimum operation cost. It adds realistic precedence and transit time constraints on the pairing of vehicles and customers. This paper tackles the DARP with time windows (DARPTW) from a new and innovative angle as it combines hybridization techniques with an adaptive large neighborhood search heuristic algorithm. The main objective is to improve the overall real-life performance of vehicle routing operations. Real-life data are refined and fed to a hybrid adaptive large neighborhood search (Hybrid-ALNS) algorithm which provides a near-optimal routing solution. The computational results on real-life instances, in the Canadian city of Vancouver and its region, and DARPTW benchmark instances show the potential improvements achieved by the proposed heuristic and its adaptability.