Copertina

Classical and Deep Reinforcement Learning Inventory Control Policies for Pharmaceutical Supply Chains with Perishability and Non-Stationarity

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Stranieri, Francesco, Kouki, Chaaben, van Jaarsveld, Willem, Stella, Fabio
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Artificial Intelligence Machine Learning Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains
di: Stranieri, Francesco, et al.
Pubblicazione: (2022)

Projected Inventory Level Policies for Lost Sales Inventory Systems: Asymptotic Optimality in Two Regimes
di: van Jaarsveld, Willem, et al.
Pubblicazione: (2021)

Scalable Policies for the Dynamic Traveling Multi-Maintainer Problem with Alerts
di: Verleijsdonk, Peter, et al.
Pubblicazione: (2024)

Maintenance Optimization for Asset Networks with Unknown Degradation Parameters
di: Verleijsdonk, Peter, et al.
Pubblicazione: (2024)

Stochastic Optimization of Inventory at Large-scale Supply Chains
di: Jin, Zhaoyang Larry, et al.
Pubblicazione: (2025)

Stable Spare Parts Pooling for Military Weapon Systems
di: Schlicher, Loe, et al.
Pubblicazione: (2018)

Automated decision-making for dynamic task assignment at scale
di: Bianco, Riccardo Lo, et al.
Pubblicazione: (2025)

Same-day or next-day? Transparent time-dependent shipment pricing for e-fulfillment
di: Mohring, Uta, et al.
Pubblicazione: (2023)

Automatic Policy Search using Population-Based Hyper-heuristics for the Integrated Procurement and Perishable Inventory Problem
di: Felizardo, Leonardo Kanashiro, et al.
Pubblicazione: (2025)

Effects of Geopolitical Strain on Global Pharmaceutical Supply Chain Design and Drug Shortages
di: De La Pava, Martha L. Sabogal, et al.
Pubblicazione: (2023)

Closing the Loop: Coordinating Inventory and Recommendation via Deep Reinforcement Learning on Multiple Timescales
di: Jiang, Jinyang, et al.
Pubblicazione: (2025)

Constrained Reinforcement Learning for the Dynamic Inventory Routing Problem under Stochastic Supply and Demand
di: Hasturk, Umur, et al.
Pubblicazione: (2025)

Enhancing Pharmaceutical Cold Supply Chain: Integrating Medication Synchronization and Diverse Delivery Modes
di: Potters, Elise, et al.
Pubblicazione: (2024)

Coordinated Communication and Inventory Optimization in Multi-Retailer Supply Chains
di: Sudhakara, Sagar, et al.
Pubblicazione: (2025)

Minimizing Material Waste in Additive Manufacturing through Online Reel Assignment
di: Celenk, Ilayda, et al.
Pubblicazione: (2026)

Optimization of inventory and capacity in large-scale assembly systems using extreme-value theory
di: Meijer, Mirjam S., et al.
Pubblicazione: (2021)

A Stability Principle for Learning under Non-Stationarity
di: Huang, Chengpiao, et al.
Pubblicazione: (2023)

IoT-based Fresh Produce Supply Chain Under Uncertainty: An Adaptive Optimization Framework
di: Seth, Chirag, et al.
Pubblicazione: (2025)

Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm
di: Li, Yueyi, et al.
Pubblicazione: (2024)

Structure-Informed Deep Reinforcement Learning for Inventory Management
di: Maggiar, Alvaro, et al.
Pubblicazione: (2025)

An Inventory System with Two Supply Modes and Lévy Demand
di: Pérez, José Luis, et al.
Pubblicazione: (2025)

Optimal Chaining of Vehicle Plans with Time Windows
di: Fiedler, David, et al.
Pubblicazione: (2024)

Hybrid Quantum-Classical Optimization for Multi-Objective Supply Chain Logistics
di: Heese, Raoul, et al.
Pubblicazione: (2026)

Towards Autonomous Supply Chains: Definition, Characteristics, Conceptual Framework, and Autonomy Levels
di: Xu, Liming, et al.
Pubblicazione: (2023)

The Price of Simplicity: Analyzing Decoupled Policies for Multi-Location Inventory Control
di: John, Yohan, et al.
Pubblicazione: (2025)

Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
di: Tang, Yu, et al.
Pubblicazione: (2026)

Optimizing Perishable and Non-Perishable Product Assignment to Packaging Lines in a Sustainable Manufacturing System: An AUGMECON2VIKOR Algorithm
di: Shahabi-Shahmiri, Reza, et al.
Pubblicazione: (2024)

Learning to Adapt: In-Context Learning Beyond Stationarity
di: Qin, Zhen, et al.
Pubblicazione: (2026)

Goldstein Stationarity in Lipschitz Constrained Optimization
di: Grimmer, Benjamin, et al.
Pubblicazione: (2023)

R2L: Reliable Reinforcement Learning: Guaranteed Return & Reliable Policies in Reinforcement Learning
di: Farhi, Nadir
Pubblicazione: (2025)

An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories
di: Harsha, Pavithra, et al.
Pubblicazione: (2023)

Online Policy Selection for Inventory Problems
di: Hihat, Massil, et al.
Pubblicazione: (2024)

Performative Policy Gradient: Optimality in Performative Reinforcement Learning
di: Basu, Debabrota, et al.
Pubblicazione: (2025)

Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming
di: Bertsekas, Dimitri P.
Pubblicazione: (2024)

CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound
di: Anand, Akhil S, et al.
Pubblicazione: (2025)

Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
di: Klein, Sara, et al.
Pubblicazione: (2023)

Non-Stationary Inventory Control with Lead Times
di: Amiri, Nele H., et al.
Pubblicazione: (2026)

Deep Reinforcement Learning for Traveling Purchaser Problems
di: Yuan, Haofeng, et al.
Pubblicazione: (2024)

Designing Information Delays in Supply Chains
di: Talwai, Prem, et al.
Pubblicazione: (2026)

Deep Reinforcement Learning Optimization for Uncertain Nonlinear Systems via Event-Triggered Robust Adaptive Dynamic Programming
di: Bai, Ningwei, et al.
Pubblicazione: (2025)