Coordinated optimization of departure sequencing and section-track allocation in railway short-term concentrated departure scenarios based on qubo and hybrid quantum algorithms

• URL: http://arxiv.org/abs/2606.06543v1
• Date: Thu, 04 Jun 2026 06:23:13 GMT
• Title: Coordinated optimization of departure sequencing and section-track allocation in railway short-term concentrated departure scenarios based on qubo and hybrid quantum algorithms
• Abstract summary: This study examines the coordinated optimization of departure sequencing and section-track allocation in railway short-term concentrated departure scenarios.<n>A quadratic unconstrained binary optimization model is formulated to represent departure-position assignment and section-track selection.<n>A simulation-based evaluation layer is introduced to assess section occupation, intermediate-station waiting, platform-capacity pressure, running-time fluctuations, and delay propagation.
• Score: 3.045106532420546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study examines the coordinated optimization of departure sequencing and section-track allocation in railway short-term concentrated departure scenarios. A quadratic unconstrained binary optimization (QUBO) model is formulated to represent departure-position assignment and section-track selection within a unified binary framework. Because the quality of a dispatching scheme depends on time-dependent operational interactions that cannot be fully captured by a static combinatorial model, a simulation-based evaluation layer is introduced to assess section occupation, intermediate-station waiting, platform-capacity pressure, running-time fluctuations, and delay propagation. Within this layered framework, conventional heuristics, quantum-inspired algorithms, and hybrid algorithms are compared on the same decision structure. The results show that the QUBO model can generate feasible candidate schemes after decoding, while the simulation layer clearly differentiates the operational performance of the competing algorithms under both normal and disturbed conditions. In the tested scenarios, QPSO-QAOA performs best under normal conditions, and the quantum-enhanced methods reduce comprehensive cost by 4.28\%--26.26\% and total delay by 4.37\%--24.25\% on average under dynamic conditions relative to their conventional counterparts. These findings suggest that the integration of QUBO-based modeling and simulation-based evaluation provides a useful methodological framework for railway short-term concentrated departure scheduling, although validation with real operational data remains necessary.

Related papers

• From Atoms to Entropy: Optimal Noise Allocation for Diffusion Training in the Convex Regime [52.459513910242485]
  We develop a general statistical framework for studying optimal noise-level allocation in diffusion training.<n>We show that the optimized training schedule admits an atomic minimizer, concentrated on finitely many noise levels.<n>We then evaluate the entropic schedule in larger-scale experiments, where full schedule optimization is currently intractable.
  arXiv  Detail & Related papers  (2026-07-10T20:59:06Z)
• Adaptive Reservoir Computing for Multi-Scenario Chaotic System Forecasting [0.0]
  We present an adaptive reservoir computing framework for the CTF-4-Science Lorenz benchmark.<n>We tailor the training and prediction procedure of Echo State Networks (ESNs) to the specific demands of each evaluation scenario.<n>The proposed framework achieves a score of 74.91 on the public benchmark leaderboard.
  arXiv  Detail & Related papers  (2026-05-27T08:27:02Z)
• Sampling-Based Control via Entropy-Regularized Optimal Transport [18.278741443124655]
  OT-MPC is a sampling-based algorithm that overcomes limitations through an entropy-regularized optimal transport formulation.<n>We derive closed-form, gradient-free updates via the Sinkhorn algorithm, enabling real-time performance.
  arXiv  Detail & Related papers  (2026-05-04T02:07:15Z)
• Para-B&B: Load-Balanced Deterministic Parallelization of Solving MIP [50.917107318582715]
  Mixed-integer programming (MIP) extends linear programming by incorporating both continuous and integer decision variables.<n>This paper presents the first fully open-source implementation of deterministic parallel branch-and-bound for HiGHS, a high-performance MIP solver.<n>Our approach introduces a novel data-parallel architecture ensuring strict determinism by replicating complete solver state across worker threads.
  arXiv  Detail & Related papers  (2026-02-10T14:17:53Z)
• Temporal Pair Consistency for Variance-Reduced Flow Matching [13.328987133593154]
  Temporal Pair Consistency (TPC) is a lightweight variance-reduction principle that couples velocity predictions at paired timesteps along the same probability path.<n>Instantiated within flow matching, TPC improves sample quality and efficiency across CIFAR-10 and ImageNet at multiple resolutions.
  arXiv  Detail & Related papers  (2026-02-04T00:05:21Z)
• Approximate Quantum State Preparation with Tree-Based Bayesian Optimization Surrogates [4.946006905837039]
  We study the problem of approximate state preparation on near-term quantum computers.<n>The goal is to construct a parameterized circuit that reproduces the output distribution of a target quantum state while minimizing resource overhead.<n>We propose CircuitTree, a surrogate-guided optimization framework based on Bayesian Optimization with tree-based models.
  arXiv  Detail & Related papers  (2025-09-30T18:19:37Z)
• Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios [4.735413508037063]
  This paper proposes a momentum-constrained hybrid trajectory optimization framework (MHHTOF) tailored for assistive navigation in visually impaired scenarios.<n>It integrates trajectory sampling generation, optimization and evaluation with residual deep reinforcement learning (DRL)<n> Experimental results demonstrate that the proposed LSTM-BResPPO achieves significantly faster convergence, attaining stable policy performance in approximately half the training required by the PPO.
  arXiv  Detail & Related papers  (2025-09-19T04:33:39Z)
• A First-order Generative Bilevel Optimization Framework for Diffusion Models [57.40597004445473]
  Diffusion models iteratively denoise data samples to synthesize high-quality outputs.<n>Traditional bilevel methods fail due to the infinite-dimensional probability space and prohibitive sampling costs.<n>We formalize this challenge as a generative bilevel optimization problem.<n>Our first-order bilevel framework overcomes the incompatibility of conventional bilevel methods with diffusion processes.
  arXiv  Detail & Related papers  (2025-02-12T21:44:06Z)
• Higher Performance Visual Tracking with Dual-Modal Localization [106.91097443275035]
  Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy. We propose a dual-modal framework for target localization, consisting of robust localization suppressingors via ONR and the accurate localization attending to the target center precisely via OFC.
  arXiv  Detail & Related papers  (2021-03-18T08:47:56Z)
• Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting [41.94295877935867]
  We present application-driven learning, a new closed-loop framework in which the processes of forecasting and decision-making are merged and co-optimized. We show that the proposed methodology is scalable and yields consistently better performance than the standard open-loop approach.
  arXiv  Detail & Related papers  (2021-02-26T02:43:28Z)
• Comparing Probability Distributions with Conditional Transport [63.11403041984197]
  We propose conditional transport (CT) as a new divergence and approximate it with the amortized CT (ACT) cost. ACT amortizes the computation of its conditional transport plans and comes with unbiased sample gradients that are straightforward to compute. On a wide variety of benchmark datasets generative modeling, substituting the default statistical distance of an existing generative adversarial network with ACT is shown to consistently improve the performance.
  arXiv  Detail & Related papers  (2020-12-28T05:14:22Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.