mlsanityData to 14 Oct 2026

Optimization of the Railcar Assignment Problem Using Zone-based Double Deep Reinforcement Learning

arXiv 2608.18442 ↗First posted 19 Aug 2026math.OCcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Railcar switching, or shunting operations decisions play a significant role in the efficient operation of railyard systems, which are in turn critical to the fast and effective movement of goods. In flat yards, switching operations are primarily performed using locomotives to push and pull railcars in order to assemble and disassemble trains. In such settings, railcars with predefined destinations are located across multiple parallel rail tracks, and must be moved, or switched, in order to form desired outbound trains. This study addresses the Railcar Assignment Problem (RAP) in flat yards with an objective of minimizing the total number of switching movements. We present a novel mixed-integer programming (MIP) model for this problem that incorporates practical operational constraints in rail yards, and demonstrate its NP-hardness. To solve large-scale instances, we propose a comprehensive Zone-based Double Deep Q-Network (Zone-DDQN) heuristic method that integrates railway structure, yard-zone decomposition, and a Double Deep Q-Network (DDQN). The yard-zone decomposition strategy partitions the yard into multiple parallel yard zones, after which the DDQN is applied to solve the problem within each zone individually and sequentially. Computational experiments across small-, medium-, and large-scale yards were conducted on a series of RAP instances. Average results show that the Zone-DDQN heuristic achieves an average optimality gap of 5.71% across small-scale yard instances. For large-scale yard instances containing more than 150 railcars and 30 tracks, the MIP model was not able to obtain solutions within 24 hours. In contrast, the Zone-DDQN heuristic was able to solve these instances with an average running time of 214.42 seconds.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 32 references that are in the map. First posted on arXiv 19 Aug 2026.

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5 of 32 references.

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Same direction, newest first

  1. 2026A Novel Hybrid Heuristic-Reinforcement Learning Optimization Approach for a Class of Railcar Shunting Problems5 Mar

Other references · 4

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2019A Novel Dynamic Programming Approach to the Train Marshalling Problemcited by 3
  3. 2025Optimizing Railcar Movements to Create Outbound Trains in a Freight Railyardcited by 2
  4. 2022Sorting Balls and Water: Equivalence and Computational Complexitycited by 1

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  5. 2026Two-Stage Learned Decomposition for Scalable Routing on Multigraphs
  6. 2026Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management
  7. 2025GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding
  8. 2024Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes