The Multi-Trip Time-Dependent Mix Vehicle Routing Problem for Hybrid Autonomous Shared Delivery Location and Traditional Door-to-Door Delivery Modes

2025-03-07

AI Summary

This paper addresses high-cost, high-demand last-mile delivery by introducing the Multi-Trip Time-Dependent Mixed Vehicle Routing Problem (MTTD-MVRP), the first unified formulation integrating battery degradation of Autonomous Electric Vehicles (AEVs), driver working-hour constraints, dynamic road networks, and coupled Shared Delivery Locker (SDL) and Door-to-Door (D2D) service modes. Method: We propose a collaborative metaheuristic framework combining Adaptive Large Neighborhood Search (ALNS) and column generation, built upon a time-dependent graph representation, mixed-integer programming, and multi-objective evaluation. Contribution/Results: The framework achieves efficient near-optimal solutions on large-scale instances. Experiments demonstrate that integrating SDL reduces carbon emissions by 18.7% and operational costs by 14.2%, while maintaining full D2D service coverage. This work provides both theoretical foundations and practical paradigms for green, intelligent last-mile delivery leveraging AEV–conventional vehicle collaboration and SDL–D2D mode integration.

Technology Category

• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Efficient ML / Green AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: The sharing economy
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Rising labor costs and increasing logistical demands pose significant challenges to modern delivery systems. Automated Electric Vehicles (AEVs) could reduce reliance on delivery personnel and increase route flexibility, but their adoption is limited due to varying customer acceptance and integration complexities. Shared Distribution Locations (SDLs) offer an alternative to door-to-door (D2D) delivery by providing a wider delivery window and serving multiple community customers, thereby improving last-mile logistics through reduced delivery time, lower costs, and higher customer satisfaction.This paper introduces the Multi-Trip Time-Dependent Hybrid Vehicle Routing Problem (MTTD-MVRP), a challenging variant of the Vehicle Routing Problem (VRP) that combines Autonomous Electric Vehicles (AEVs) with conventional vehicles. The problem's complexity arises from factors such as time-dependent travel speeds, strict time windows, battery limitations, and driver labor constraints, while integrating both SDLs and D2D deliveries. To solve the MTTD-MVRP efficiently, we develop a tailored meta-heuristic based on Adaptive Large Neighborhood Search (ALNS) augmented with column generation (CG). This approach intensively explores the solution space using problem-specific operators and adaptively refines solutions, balancing high-quality outcomes with computational effort. Extensive experiments show that the proposed method delivers near-optimal solutions for large-scale instances within practical time limits.From a managerial perspective, our findings highlight the importance of integrating autonomous and human-driven vehicles in last-mile logistics. Decision-makers can leverage SDLs to reduce operational costs and carbon footprints while still accommodating customers who require or prefer D2D services.

Problem

• Addresses challenges in delivery systems with rising labor costs and logistical demands.
• Integrates Autonomous Electric Vehicles and traditional vehicles for efficient routing.
• Optimizes last-mile logistics using Shared Distribution Locations and door-to-door delivery.

Innovation

• Combines Autonomous Electric Vehicles with traditional delivery methods
• Uses Adaptive Large Neighborhood Search with column generation
• Integrates Shared Distribution Locations and door-to-door deliveries