A Tutorial on Multi-time Scale Optimization Models and Algorithms
Abstract
Systems across different industries consist of interrelated processes and decisions in different time scales including long-time decisions and short-term decisions. To optimize such systems, the most effective approach is to formulate and solve multi-time scale optimization models that integrate various decision layers. In this tutorial, we provide an overview of multi-time scale optimization models and review the algorithms used to solve them. We also discuss the metric Value of the Multi-scale Model (VMM) introduced to quantify the benefits of using multi-time scale optimization models as opposed to sequentially solving optimization models from high-level to low-level. Finally, we present an illustrative example of a multi-time scale capacity expansion planning model and showcase how it can be solved using some of the algorithms (https://github.com/li-group/MultiScaleOpt-Tutorial.git). This tutorial serves as both an introductory guide for beginners with no prior experience and a high-level overview of current algorithms for solving multi-time scale optimization models, catering to experts in process systems engineering.
Cite
@article{arxiv.2502.20568,
title = {A Tutorial on Multi-time Scale Optimization Models and Algorithms},
author = {Asha Ramanujam and Can Li},
journal= {arXiv preprint arXiv:2502.20568},
year = {2025}
}