mlsanityData to 14 Oct 2026

Deep Reinforcement Learning solution for pickup and delivery routing problems with time window and capacity constraints

arXiv 2608.14156 ↗First posted 14 Aug 2026cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

The task of constructing vehicles optimal routes for pickup and delivery of goods is one of most promising tasks in the context of global urban population growth. Although this kind of problems with small size can be solved by various classical approaches, a fast (or realtime) route optimizer under the constraints of the real world (such as capacity and time windows constraints) for medium-large size problems still remains a highly challenging task. In this work we, for the first time, successfully applied a deep Reinforcing Learning approach (modified JAMPR model) to solve Pickup and Delivery problem with Capacity and Time Window constraints (CPDPTW). We obtained a robust model that gives a fast optimal solution for problems of small and medium size, and gives fast suboptimal solution for problems of larger (> 200) size.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 27 references that are in the map. First posted on arXiv 14 Aug 2026.

Builds on

10 of 27 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Pointer Networkscited by 895
  3. 2018Attention, Learn to Solve Routing Problems!cited by 656
  4. 2018Reinforcement Learning for Solving the Vehicle Routing Problemcited by 333
  5. 2018Learning to Perform Local Rewriting for Combinatorial Optimizationcited by 185
  6. 2020Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhoodcited by 125
  7. 2021Learning to Delegate for Large-scale Vehicle Routingcited by 109
  8. 2021Reinforcement Learning for Ridesharing: An Extended Surveycited by 35
  9. 2020Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attentioncited by 29
  10. 2019ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problemscited by 21

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  1. 2026Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry
  2. 2024Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II
  3. 2025Deep Reinforcement Learning for Solving the Fleet Size and Mix Vehicle Routing Problem
  4. 2024Learn to Solve Vehicle Routing Problems ASAP: A Neural Optimization Approach for Time-Constrained Vehicle Routing Problems with Finite Vehicle Fleet
  5. 2024Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows
  6. 2025Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning
  7. 2024Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes
  8. 2026Integrated Order Dispatching and Routing for Last-Mile Pickup via Deep Reinforcement Learning