Copertina

Business Process Simulation: Probabilistic Modeling of Intermittent Resource Availability and Multitasking Behavior

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	López-Pintado, Orlenys, Dumas, Marlon
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Discovery and Simulation of Data-Aware Business Processes
di: López-Pintado, Orlenys, et al.
Pubblicazione: (2024)

Optimization of Activity Batching Policies in Business Processes
di: López-Pintado, Orlenys, et al.
Pubblicazione: (2025)

Enhancing Business Process Simulation Models with Extraneous Activity Delays
di: Chapela-Campa, David, et al.
Pubblicazione: (2022)

Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach
di: Shoush, Mahmoud, et al.
Pubblicazione: (2023)

How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestamps
di: Ali, Muhammad Awais, et al.
Pubblicazione: (2025)

Probabilistic Prediction Markets with Intermittent Contributions
di: Vitali, Michael, et al.
Pubblicazione: (2025)

Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling
di: Dey, Sharmita, et al.
Pubblicazione: (2024)

A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation
di: Kirchdorfer, Lukas, et al.
Pubblicazione: (2025)

Online Discovery of Simulation Models for Evolving Business Processes (Extended Version)
di: Vinci, Francesco, et al.
Pubblicazione: (2025)

Towards Understanding the Benefit of Multitask Representation Learning in Decision Process
di: Lu, Rui, et al.
Pubblicazione: (2025)

CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction
di: Sadia, Rabeya Tus, et al.
Pubblicazione: (2025)

A Rollout-Based Algorithm and Reward Function for Resource Allocation in Business Processes
di: Middelhuis, Jeroen, et al.
Pubblicazione: (2025)

Agentic Business Process Management Systems
di: Dumas, Marlon, et al.
Pubblicazione: (2026)

Unsupervised Work Behavior Pattern Extraction Based on Hierarchical Probabilistic Model
di: Saito, Issei, et al.
Pubblicazione: (2024)

nnterp: A Standardized Interface for Mechanistic Interpretability of Transformers
di: Dumas, Clément
Pubblicazione: (2025)

Taylorformer: Probabilistic Modelling for Random Processes including Time Series
di: Nivron, Omer, et al.
Pubblicazione: (2023)

Omni-Mol: Multitask Molecular Model for Any-to-any Modalities
di: Hu, Chengxin, et al.
Pubblicazione: (2025)

What's Coming Next? Short-Term Simulation of Business Processes from Current State
di: Avramenko, Maksym, et al.
Pubblicazione: (2025)

Privacy in Metalearning and Multitask Learning: Modeling and Separations
di: Aliakbarpour, Maryam, et al.
Pubblicazione: (2024)

Can Multitask Learning Enhance Model Explainability?
di: Najjar, Hiba, et al.
Pubblicazione: (2025)

Exact and general decoupled solutions of the LMC Multitask Gaussian Process model
di: Truffinet, Olivier, et al.
Pubblicazione: (2023)

Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing
di: Chapela-Campa, David, et al.
Pubblicazione: (2024)

Relaxed Equivariance via Multitask Learning
di: Elhag, Ahmed A., et al.
Pubblicazione: (2024)

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference
di: Chang, Paul E., et al.
Pubblicazione: (2024)

Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks
di: Dai, Lu, et al.
Pubblicazione: (2025)

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
di: Chen, Yifan, et al.
Pubblicazione: (2024)

Contextual Bandits for Resource-Constrained Devices using Probabilistic Learning
di: Angioli, Marco, et al.
Pubblicazione: (2026)

Offline Multitask Representation Learning for Reinforcement Learning
di: Ishfaq, Haque, et al.
Pubblicazione: (2024)

Multitask Online Learning: Listen to the Neighborhood Buzz
di: Achddou, Juliette, et al.
Pubblicazione: (2023)

Learning Model Representations Using Publicly Available Model Hubs
di: Falk, Damian, et al.
Pubblicazione: (2025)

Data-Efficient Multitask DAgger
di: Fu, Haotian, et al.
Pubblicazione: (2025)

Multitask Learning with Stochastic Interpolants
di: Negrel, Hugo, et al.
Pubblicazione: (2025)

Taxonomy-Conditioned Hierarchical Bayesian TSB Models for Heterogeneous Intermittent Demand Forecasting
di: Bai, Zong-Han, et al.
Pubblicazione: (2025)

Decoupled SGDA for Games with Intermittent Strategy Communication
di: Zindari, Ali, et al.
Pubblicazione: (2025)

Self-Explaining Neural Networks for Business Process Monitoring
di: Bassan, Shahaf, et al.
Pubblicazione: (2025)

Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes
di: Ladopoulou, Domniki, et al.
Pubblicazione: (2025)

Task Weighting through Gradient Projection for Multitask Learning
di: Bohn, Christian, et al.
Pubblicazione: (2024)

Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets
di: Raymond, Matt, et al.
Pubblicazione: (2024)

Multitask Learning Can Improve Worst-Group Outcomes
di: Kulkarni, Atharva, et al.
Pubblicazione: (2023)

Predictive Monitoring of Business Processes
di: Maggi, Fabrizio Maria, et al.
Pubblicazione: (2013)