This transition moves inventory planning away from static safety stock rules toward more flexible policy structures that ...

Abstract: This paper presents a computation-efficient stochastic dynamic programming algorithm for solving energy storage price arbitrage considering variable charge and discharge efficiencies. We ...

Stochastic processes involving randomness dominate in many areas of natural and man made systems. Describing their evolution quantitatively requires powerful theory from the fields of probability, ...

Abstract: In this study, we use a stochastic representation of wind for medium/long-term planning problems that are associated with the operation of hydro-thermal systems. The stochastic dual dynamic ...

This project implements a Dynamic Programming (DP) solution for optimal inventory control, inspired by fundamental principles in Dimitri Bertsekas's work on "Lessons from AlphaZero for Optimal, Model ...

This is an mini-course on "Deep Learning for Solving Dynamic Stochastic Models", held from Wednesday, May 22nd, 2024 2 - Friday, May 24th, 2024 at Central-German Doctoral Program Economics, University ...

Discovering the rules of synaptic plasticity is an important step for understanding brain learning. Existing plasticity models are either (1) top-down and interpretable, but not flexible enough to ...

A two-layer multi-time scale stochastic production simulation framework is constructed to account for the long-term contract electricity quantity of ultra-high voltage direct current (UHVDC) ...