Incentive-aware Electric Vehicle Routing Problem: a Bi-level Model and a Joint Solution Algorithm

TL;DR

This paper introduces an incentive-aware electric vehicle routing model that jointly optimizes routes, charging, and customer incentives, leading to cost reductions and customer savings in freight transportation.

Contribution

It presents a novel bi-level model and a transformation method to efficiently solve incentive-based routing and charging problems for electric vehicles.

Findings

Operational costs reduced by up to 5%

Customers save over 30% in delivery fees

Effective joint optimization improves fleet efficiency

Abstract

Fixed pickup and delivery times can strongly limit the performance of freight transportation. Against this backdrop, fleet operators can use compensation mechanisms such as monetary incentives to buy delay time from their customers, in order to improve the fleet efficiency and ultimately minimize the costs of operation. To make the most of such an operational model, the fleet activities and the incentives should be jointly optimized accounting for the customers' reactions. Against this backdrop, this paper presents an incentive-aware electric vehicle routing scheme in which the fleet operator actively provides incentives to the customers in exchange of pickup or delivery time flexibility. Specifically, we first devise a bi-level model whereby the fleet operator optimizes the routes and charging schedules of the fleet jointly with an incentive rate to reimburse the delivery delays experienced by the customers. At the same time, the customers choose the admissible delays by minimizing a monetarily-weighted combination of the delays minus the reimbursement offered by the operator. Second, we tackle the complexity resulting from the bi-level and nonlinear problem structure with an equivalent transformation method, reformulating the problem as a single-level optimization problem that can be solved with standard mixed-integer linear programming algorithms. We demonstrate the effectiveness of our framework via extensive numerical experiments using VRP-REP data from Belgium. Our results show that by jointly optimizing routes and incentives subject to the customers' preferences, the operational costs can be reduced by up to 5%, whilst customers can save more than 30% in total delivery fees.