A Tutorial on Multi-time Scale Optimization Models and Algorithms
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917966264664064 |
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| author | Ramanujam, Asha Li, Can |
| author_facet | Ramanujam, Asha Li, Can |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20568 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Tutorial on Multi-time Scale Optimization Models and Algorithms Ramanujam, Asha Li, Can Optimization and Control 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. |
| title | A Tutorial on Multi-time Scale Optimization Models and Algorithms |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2502.20568 |