Maria Bresich | AC Group | TU Wien

A Reinforcement Learning Guided Large Neighborhood Search for the Dynamic Electric Autonomous Dial-a-Ride Problem

Thu, 01 Jan 2026 00:00:00 +0000

Determining Destroy Sets in Large Neighborhood Search by Generative Flow Networks

Thu, 01 Jan 2026 00:00:00 +0000

Revisiting Large Neighborhood Search with On-the-Fly ChargingStation Insertion for the Electric Autonomous Dial-a-Ride Problem

Thu, 01 Jan 2026 00:00:00 +0000

Search Space Reduction Through Machine Learning for the Electric Autonomous Dial-A-Ride Problem

Thu, 01 Jan 2026 00:00:00 +0000

Improvements in Large Neighborhood Search for the Electric Autonomous Dial-a-Ride Problem

Wed, 01 Jan 2025 00:00:00 +0000

Learning to Solve Dynamic Vehicle Routing Problems

Mon, 01 Jan 2024 00:00:00 +0000

a joined research project from the Algorithms and Complexity Group, TU Wien, Austria, and Honda Research Institute, Germany

Project Team

Topic

Vehicle routing - i.e., planning an optimal set of routes for a fleet of vehicles - is an intensely studied research area with enormous practical and raising relevance due to increasing mobility and transportation demand and new challenges coming, e.g., from increasing interest in shared mobility services and electric vehicles. The dial-a-ride problem (DARP), for example, is the problem of finding optimal tours of vehicles through different pickup and drop-off locations in order to serve a number of transportation requests, allowing different customers to share a vehicle. The electric autonomous dial-a-ride problem (E-ADARP) represents a challenging and practically relevant extension to the DARP, where electric autonomous vehicles are employed and their charging requirements have to be taken into consideration. Furthermore, not only classical objectives like total travel time have to be optimized, but user inconvenience also plays an important role. Thus, factors such as user excess ride time, which is due to detours because of the ride-sharing, have to be taken into account.

For such problems, heuristic optimization approaches are considered to be the means of choice due to a better scalability compared to exact approaches. In this project, we propose a heuristic framework based on large neighborhood search (LNS) to solve the E-ADARP, and we plan to tackle the issue of scalability by automatically designing, i.e., learning, efficient heuristics that either guide or possibly replace classical optimization techniques. We will investigate the usage of reinforcement learning with different machine learning models to dynamically select operators for the LNS from a set of possible operators during the optimization process. We intend to experimentally compare this approach to other learning techniques such as classical supervised learning, imitation learning, and Q learning.

Letting a Large Neighborhood Search for an Electric Dial-A-Ride Problem Fly: On-The-Fly Charging Station Insertion

Mon, 01 Jan 2024 00:00:00 +0000

Mixed Integer Linear Programming Based Large Neighborhood Search Approaches for the Directed Feedback Vertex Set Problem

Mon, 01 Jan 2024 00:00:00 +0000

Hybrid Metaheuristics Based on Large Neighborhood Search and Mixed Integer Linear Programming for the Directed Feedback Vertex Set Problem

Sun, 01 Jan 2023 00:00:00 +0000