Human-augmented prescriptive analytics with interactive multi-objective reinforcement learning
K Lepenioti, A Bousdekis, D Apostolou… - IEEE Access, 2021 - ieeexplore.ieee.org
The rise of Artificial Intelligence (AI) enables enterprises to manage large amounts of data in
order to derive predictions about future performance and to gain meaningful insights. In this …
order to derive predictions about future performance and to gain meaningful insights. In this …
예산제약을고려한다기간Newsvendor 문제에서의Q-learning 기법적용
박나희， 민대기 - 한국경영과학회지, 2022 - dbpia.co.kr
This paper considers a multi-period, multi-item Newsvendor problem under budget
constraints in which a decision-maker orders items with aims to minimize the total inventory …
constraints in which a decision-maker orders items with aims to minimize the total inventory …
Deep-Reinforcement Learning-Based Architecture for Multi-Objective Optimization of Stock Prediction
R Jyothi, GN Krishnamurthy - European Journal of Electrical Engineering …, 2022 - ejece.org
Artificial Intelligence has been established to predict the future performance of the trading in
modern era including Statistics, Computer Science, and economics, especially the stack …
modern era including Statistics, Computer Science, and economics, especially the stack …
[PDF][PDF] Reinforcement Learning for Perishable & Deteriorating Multi-Echelon Inventory
JAS Mapes - 2020 - research.tue.nl
This thesis explores a reinforcement learning approach in a pharmaceutical supply chain.
The Operations Planning Algorithm (OPA) is a non-parametric data-driven recursive …
The Operations Planning Algorithm (OPA) is a non-parametric data-driven recursive …