Hybrid Memetic Search for Electric Vehicle Routing with Time Windows, Simultaneous Pickup-Delivery, and Partial Recharges

TL;DR

This paper introduces a hybrid memetic algorithm for the complex electric vehicle routing problem with time windows, pickups, deliveries, and partial recharges, demonstrating superior performance and providing a new large-scale benchmark for real-world applications.

Contribution

The paper proposes a novel hybrid memetic algorithm with two innovative components for EVRP with time windows, pickups, deliveries, and partial recharges, and introduces a new large-scale benchmark dataset.

Findings

HMA outperforms existing algorithms on diverse instances.

The new benchmark set reflects real-world complexity.

Open-source tools facilitate further research.

Abstract

With growing environmental concerns, electric vehicles for logistics have gained significant attention within the computational intelligence community in recent years. This work addresses an emerging and significant extension of the electric vehicle routing problem (EVRP), namely EVRP with time windows, simultaneous pickup-delivery, and partial recharges (EVRP-TW-SPD), which has widespread real-world applications. We propose a hybrid memetic algorithm (HMA) for solving EVRP-TW-SPD. HMA incorporates two novel components: a parallel-sequential station insertion (PSSI) procedure for handling partial recharges that can better avoid local optima compared to purely sequential insertion, and a cross-domain neighborhood search (CDNS) that explores solution spaces of both electric and non-electric problem domains simultaneously. These components can also be easily applied to various EVRP variants. To bridge the gap between existing benchmarks and real-world scenarios, we introduce a new, large-scale EVRP-TW-SPD benchmark set derived from real-world applications, containing instances with many more customers and charging stations than existing benchmark instances. Extensive experiments demonstrate the significant performance advantages of HMA over existing algorithms across a wide range of problem instances. Both the benchmark set and HMA are to be made open-source to facilitate further research in this area.