Learn to Solve Vehicle Routing Problems ASAP: A Neural Optimization Approach for Time-Constrained Vehicle Routing Problems with Finite Vehicle Fleet
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Abstract (arXiv)
Finding a feasible and prompt solution to the Vehicle Routing Problem (VRP) is a prerequisite for efficient freight transportation, seamless logistics, and sustainable mobility. Traditional optimization methods reach their limits when confronted with the real-world complexity of VRPs, which involve numerous constraints and objectives. Recently, the ability of generative Artificial Intelligence (AI) to solve combinatorial tasks, known as Neural Combinatorial Optimization (NCO), demonstrated promising results, offering new perspectives. In this study, we propose an NCO approach to solve a time-constrained capacitated VRP with a finite vehicle fleet size. The approach is based on an encoder-decoder architecture, formulated in line with the Policy Optimization with Multiple Optima (POMO) protocol and trained via a Proximal Policy Optimization (PPO) algorithm. We successfully trained the policy with multiple objectives (minimizing the total distance while maximizing vehicle utilization) and evaluated it on medium and large instances, benchmarking it against state-of-the-art heuristics. The method is able to find adequate and cost-efficient solutions, showing both flexibility and robust generalization. Finally, we provide a critical analysis of the solution generated by NCO and discuss the challenges and opportunities of this new branch of intelligent learning algorithms emerging in optimization science, focusing on freight transportation.
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References · 25
- 2017Attention Is All You Needcited by 59,932
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2016OpenAI Gymcited by 1,684
- 2015Pointer Networkscited by 882
- 2018Attention, Learn to Solve Routing Problems!cited by 646
- 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 600
- 2018Reward Constrained Policy Optimizationcited by 397
- 2020POMO: Policy Optimization with Multiple Optima for Reinforcement Learningcited by 341
- 2018Reinforcement Learning for Solving the Vehicle Routing Problemcited by 327
- 2019An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problemcited by 265
- 2019Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problemcited by 117
- 2020Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learningcited by 100
13 more
- 2020Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problemscited by 100
- 2023RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmarkcited by 70
- 2023PyVRP: a high-performance VRP solver packagecited by 50
- 2021Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problemcited by 31
- 2020A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problemscited by 30
- 2020Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attentioncited by 29
- 2022Learning to Solve Vehicle Routing Problems: A Surveycited by 16
- 2024Distance-aware Attention Reshaping: Enhance Generalization of Neural Solver for Large-scale Vehicle Routing Problemscited by 15
- 2021Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problemscited by 13
- 2020Constrained Combinatorial Optimization with Reinforcement Learningcited by 8
- 2022A deep learning Attention model to solve the Vehicle Routing Problem and the Pick-up and Delivery Problem with Time Windowscited by 4
- 2023RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation Researchcited by 4
- 2019An analytical bound on the fleet size in vehicle routing problems: a dynamic programming approachcited by 1
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