Dynamic Vehicle Routing Problem with Prompt Confirmation of Advance Requests

2026-03-08

AI Summary

This study addresses the challenge faced by public transit agencies in simultaneously providing immediate confirmation for pre-booked on-demand ride requests and maintaining continuous route optimization. To this end, the authors propose a novel dynamic vehicle routing approach that, for the first time, unifies real-time service feasibility verification with long-term scheduling optimization within a single framework. The method integrates a reinforcement learning–based non-myopic objective function with fast insertion heuristics and anytime optimization algorithms. Experimental evaluation on a real-world U.S. microtransit dataset demonstrates that the proposed approach significantly increases the number of served requests while meeting stringent requirements for immediate response and high service reliability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Transit agencies that operate on-demand transportation services have to respond to trip requests from passengers in real time, which involves solving dynamic vehicle routing problems with pick-up and drop-off constraints. Based on discussions with public transit agencies, we observe a real-world problem that is not addressed by prior work: when trips are booked in advance (e.g., trip requests arrive a few hours in advance of their requested pick-up times), the agency needs to promptly confirm whether a request can be accepted or not, and ensure that accepted requests are served as promised. State-of-the-art computational approaches either provide prompt confirmation but lack the ability to continually optimize and improve routes for accepted requests, or they provide continual optimization but cannot guarantee serving all accepted requests. To address this gap, we introduce a novel problem formulation of dynamic vehicle routing with prompt confirmation and continual optimization. We propose a novel computational approach for this vehicle routing problem, which integrates a quick insertion search for prompt confirmation with an anytime algorithm for continual optimization. To maximize the number requests served, we train a non-myopic objective function using reinforcement learning, which guides both the insertion and the anytime algorithms towards optimal, non-myopic solutions. We evaluate our computational approach on a real-world microtransit dataset from a public transit agency in the U.S., demonstrating that our proposed approach provides prompt confirmation while significantly increasing the number of requests served compared to existing approaches.

Problem

• Dynamic Vehicle Routing Problem
• Prompt Confirmation
• Advance Requests
• On-demand Transit
• Continual Optimization

Innovation

• dynamic vehicle routing
• prompt confirmation
• continual optimization
• reinforcement learning
• non-myopic objective