On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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6 results for “Rescheduling”

Bayesian dynamic scheduling of multipurpose batch processes under incomplete look-ahead informationcs.LG

Bayesian method optimizes rescheduling for multipurpose batch processes with incomplete look-ahead information.

problem Optimizing rescheduling for multipurpose batch processes under incomplete look-ahead information.

method Proposes a Bayesian dynamic scheduling method that learns from disturbances and updates schedules online.

result Achieves statistically better long-term costs and system nervousness compared to existing periodic rescheduling strategies.

Bayesian Networks Rescheduling Multipurpose Batch Processes Incomplete Look-Ahead Dynamic Scheduling

2025-11-30abs ↗pdf ↗

Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train ticketsecon.GN

Study analyzes how discounts affect train ticket purchases and rescheduling in Switzerland.

problem Understanding how discounts influence train ticket buying and rescheduling behavior.

method Machine learning techniques, including causal machine learning, to analyze survey data.

result Increasing a discount rate by 1% increases the rescheduled trip share by 0.16% among always buyers.

train tickets discounts machine learning Switzerland rescheduling

2021-05-04abs ↗pdf ↗

LineFlow: A Framework to Learn Active Control of Production Linescs.LG

LineFlow is a framework for training RL agents to control production lines.

problem Designing control systems for production lines is challenging.

method Introduces LineFlow, an extensible Python framework for simulating and training RL agents.

result RL agents approach optimal performance in well-understood scenarios but face challenges in complex industrial lines.

production lines reinforcement learning active control worker reallocation rescheduling

2025-05-10abs ↗pdf ↗

Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscs.LG

Federated learning (FL) is a distributed deep learning method which enables multiple participants, such as mobile phones and IoT devices, to contribute a neural network model while their private training data remains in local devices. This distributed approach is promising in the edge computing system where have a larg…

2019-07-02abs ↗pdf ↗

ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Banditscs.LG

Improved algorithm for adaptive dueling bandits with near-optimal regret bound.

problem Non-stationary dueling bandits with unknown number of preference changes.

method Elimination-based rescheduling algorithm for adaptive dynamic regret.

result Near-optimal

ilde{O}(\sqrt{S^{ exttt{CW}} T})

dynamic regret bound.

dueling bandits non-stationary adaptive algorithms dynamic regret condorcet winner

2022-10-25abs ↗pdf ↗

Using a Classifier Ensemble for Proactive Quality Monitoring and Control: the impact of the choice of classifiers types, selection criterion, and fusion processcs.LG

In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product rescheduling , process reliability,..) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approa…

2018-04-05abs ↗pdf ↗