EURO-Online login
- New to EURO? Create an account
- I forgot my username and/or my password.
- Help with cookies
(important for IE8 users)
2381. Addressing Real-World Side Constraints in Combinatorial Optimization with Deep Reinforcement Learning
Invited abstract in session MC-3: (Deep) Reinforcement Learning for Combinatorial Optimization 1, stream Data Science Meets Optimization.
Monday, 12:30-14:00Room: 1005 (building: 202)
Authors (first author is the speaker)
| 1. | Nayeli Gast Zepeda
|
| Management Science & Business Analytics, Bielefeld University | |
| 2. | Kevin Tierney
|
| Decision and Operation Technologies, Bielefeld University | |
| 3. | André Hottung
|
| Decision and Operation Technologies, Bielefeld University |
Abstract
Deep Reinforcement Learning (DRL) methodologies have garnered increasing attention in addressing combinatorial optimization challenges, particularly in domains such as routing and scheduling. While recent approaches have demonstrated notable efficacy, especially in classic problems like the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP), they often operate within simplified problem settings, lacking real-world side constraints. Consequently, DRL methods encounter difficulties in generating feasible solutions for more complex scenarios. In this study, we address these limitations by introducing additional real-world side constraints and exploring diverse mechanisms to accommodate them while steering the search towards feasible solutions. Our experimentation extends to a variety of combinatorial optimization problems, including the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) and Skill-VRP, showcasing our approach's effectiveness in handling practical constraints.
Keywords
- Combinatorial Optimization
- Vehicle Routing
- Machine Learning
Status: accepted
Back to the list of papers