A Deterministic Annealing Local Search for the Electric Autonomous Dial-A-Ride Problem

TL;DR

This paper introduces a deterministic annealing heuristic for the Electric Autonomous Dial-A-Ride Problem, effectively optimizing routes with recharging constraints and minimizing total travel and ride times, outperforming existing methods on benchmark instances.

Contribution

It presents the first heuristic for the static E-ADARP, including novel methods for efficient recharging and ride time calculations, and extends the model to allow multiple recharges for improved solutions.

Findings

25 new best solutions on existing instances

45 solutions equal to the best reported

19 new solutions on larger instances

Abstract

This paper investigates the Electric Autonomous Dial-A-Ride Problem (E-ADARP), which consists in designing a set of minimum-cost routes that accommodates all customer requests for a fleet of Electric Autonomous Vehicles (EAVs). Problem-specific features of the E-ADARP include: (i) the employment of EAVs and a partial recharging policy; (ii) the weighted-sum objective function that minimizes the total travel time and the total excess user ride time. In this work, we propose a Deterministic Annealing (DA) algorithm and provide the first heuristic results for the static E-ADARP. Partial recharging (i) is handled by an exact route evaluation scheme of linear time complexity. To tackle (ii), we propose a new method that allows effective computations of minimum excess user ride time by introducing a fragment-based representation of paths. These two methods compose an exact and efficient optimization of excess user ride time for a generated E-ADARP route. To validate the performance of the DA algorithm, we compare our algorithm results to the best-reported Branch-and-Cut (B\&C) algorithm results on existing instances. Our algorithm provides 25 new best solutions and 45 equal solutions on 84 existing instances. To test the algorithm performance on larger-sized instances, we establish new instances with up to 8 vehicles and 96 requests, and we provide 19 new solutions for these instances. Our final investigation extends the state-of-the-art model and explores the effect of allowing multiple visits to recharging stations. This relaxation can efficiently improve the solution's feasibility and quality.