The paper introduces a rolling‑horizon distributionally robust model for in‑station train rescheduling that updates predictions of resource closure durations as disruptions resolve. It jointly optimizes train routes, sequences, and timings to minimize worst‑case expected completion delay, using a logic‑based Benders decomposition algorithm guided by a graph neural network (GNN‑LBBD). Experiments on 28 five‑track station instances show GNN‑LBBD reduces mean completion delay by 16.63% compared to tabu search and 20.06% compared to variable neighborhood search, and that incorporating disruption‑resolution observations cuts delay by about 21.7%.
arXiv:2606. 14582v1 Announce Type: new Abstract: Efficient route optimization play a vital role in ensuring both safety and punctuality in railway operations.
arXiv:2610.22433v1 Announce Type: new Abstract: Flexible job-shop scheduling becomes resource-coupled when a scarce vehicle fleet moves jobs between stations, because the schedule sets the transport...
arXiv:2606. 07044v1 Announce Type: new Abstract: Accurate and coherent passenger demand forecasting is essential for Urban Rail Transit (URT) operations.
arXiv:2607.11725v2 Announce Type: replace-cross Abstract: Prefabricated prefinished volumetric construction (PPVC) builds room-sized modules in factories that operate as flexible job shops. A module...
UTP-Bench is a new benchmark for uncertainty-aware travel planning that evaluates large language models on their ability to generate robust itineraries under real-world stochastic conditions. The dataset covers 504 Indian cities, incorporating attractions, restaurants, accommodations, and multi-modal transportation networks, and includes empirical delay distributions and crowd-density patterns to simulate realistic disruptions. Three new metrics—Buffer Adequacy Score, Crowd-Aware Timing Score, and Transport Delay Absorption Score—measure how well generated plans maintain robustness against transit delays and crowd variability, revealing significant gaps between state-of-the-art LLMs and human-authored itineraries.
The paper introduces a Bayesian dynamic scheduling method for multipurpose batch processes that operates under incomplete look‑ahead information. By learning a Bayesian Network from the probability distribution of disturbances, the method updates posterior distributions online and guides rescheduling decisions. Compared with a periodic rescheduling strategy, the approach yields statistically better long‑term costs and reduced system nervousness on benchmark problems.
UTP‑Bench is a new benchmark that evaluates large language models on uncertainty‑aware travel planning, incorporating real‑world data from 504 Indian cities and empirical delay and crowd patterns. It introduces three metrics—Buffer Adequacy Score, Crowd‑Aware Timing Score, and Transport Delay Absorption Score—to measure how well generated itineraries remain robust under stochastic conditions. Experiments show that current LLMs lag behind human planners in buffering, delay‑aware scheduling, and crowd sensitivity.
arXiv:2608.30520v1 Announce Type: new Abstract: Automated material handling systems in semiconductor fabs are operated by a material control system (MCS) that must schedule a relay route for every tr...
arXiv:2607. 24049v1 Announce Type: new Abstract: Short-term disturbances at major passenger railway stations alter train arrival and departure times as well as the release sequence of station resources.
arXiv:2608.28896v1 Announce Type: new Abstract: Industrial process networks do not maintain a single effective topology while operating: streams are throttled or bypassed, and units move between idle...
arXiv:2608. 02950v1 Announce Type: new Abstract: Checkpoint staffing requires accurate forecasts of when screening demand will occur, yet flight schedules record departure times rather than passenger arrival times at security checkpoints.
arXiv:2607. 11725v1 Announce Type: cross Abstract: Prefabricated prefinished volumetric construction moves most building work into module factories, whose production floor operates as a flexible job shop.