The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new
technologies emerged (including software and connected equipment) that digitise …

This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and
robuSt deciSIon SupporT systems for agile mANufacTuring environments) project …

We provide a tutorial-type review on stochastic dual dynamic programming (SDDP), 4 as
one of the state-of-the-art solution methods for large-scale multistage stochastic programs …

We investigate the stochastic multi-level lot sizing problem with a service level and in a
general setting in which it is possible to have independent demand for the components as …

In this study, we present a deep reinforcement learning framework for solving scenario-
based two-stage stochastic programming problems. Stochastic programs have numerous …

In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to
address the optimal management problem of price-maker community energy storage. As a …

The lot-sizing problem aims at determining the products to be produced and their quantities
for each time period, which is a difficult problem in production planning. This problem …

We seek to optimize the production planning of a three-echelon remanufacturing system
under uncertain input data. We consider a multi-stage stochastic integer programming …

The European project ASSISTANT will provide a set of AI-based digital twins that helps
process engineers and production planners to operate collaborative mixed-model assembly …

This study focuses on the lot sizing problem with setup carry-over in multi-item multi-echelon
capacitated production systems under uncertain customer demand for end items, as well as …