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Learning-assisted optimization for transmission switching. 传输交换的学习辅助优化。

Top (Berlin, Germany) Pub Date : 2024-01-01 Epub Date: 2024-04-10 DOI: 10.1007/s11750-024-00672-0

Salvador Pineda, Juan Miguel Morales, Asunción Jiménez-Cordero

{"title":"Learning-assisted optimization for transmission switching.","authors":"Salvador Pineda, Juan Miguel Morales, Asunción Jiménez-Cordero","doi":"10.1007/s11750-024-00672-0","DOIUrl":"https://doi.org/10.1007/s11750-024-00672-0","url":null,"abstract":"The design of new strategies that exploit methods from machine learning to facilitate the resolution of challenging and large-scale mathematical optimization problems has recently become an avenue of prolific and promising research. In this paper, we propose a novel learning procedure to assist in the solution of a well-known computationally difficult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS) problem. The DC-OTS problem consists in finding the configuration of the power network that results in the cheapest dispatch of the power generating units. With the increasing variability in the operating conditions of power grids, the DC-OTS problem has lately sparked renewed interest, because operational strategies that include topological network changes have proved to be effective and efficient in helping maintain the balance between generation and demand. The DC-OTS problem includes a set of binaries that determine the on/off status of the switchable transmission lines. Therefore, it takes the form of a mixed-integer program, which is NP-hard in general. In this paper, we propose an approach to tackle the DC-OTS problem that leverages known solutions to past instances of the problem to speed up the mixed-integer optimization of a new unseen model. Although our approach does not offer optimality guarantees, a series of numerical experiments run on a real-life power system dataset show that it features a very high success rate in identifying the optimal grid topology (especially when compared to alternative competing heuristics), while rendering remarkable speed-up factors.","PeriodicalId":75228,"journal":{"name":"Top (Berlin, Germany)","volume":"32 3","pages":"489-516"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cutting uncertain stock and vehicle routing in a sustainability forestry harvesting problem. 可持续森林采伐问题中的不确定库存和车辆路线。

Top (Berlin, Germany) Pub Date : 2023-01-01 DOI: 10.1007/s11750-022-00623-7

Adejuyigbe O Fajemisin, Steven D Prestwich, Laura Climent

引用次数: 2

Mathematical optimization models for reallocating and sharing health equipment in pandemic situations. 在疫情情况下重新分配和共享卫生设备的数学优化模型。

Top (Berlin, Germany) Pub Date : 2023-01-01 Epub Date: 2022-09-02 DOI: 10.1007/s11750-022-00643-3

Víctor Blanco, Ricardo Gázquez, Marina Leal

{"title":"Mathematical optimization models for reallocating and sharing health equipment in pandemic situations.","authors":"Víctor Blanco, Ricardo Gázquez, Marina Leal","doi":"10.1007/s11750-022-00643-3","DOIUrl":"10.1007/s11750-022-00643-3","url":null,"abstract":"In this paper we provide a mathematical programming based decision tool to optimally reallocate and share equipment between different units to efficiently equip hospitals in pandemic emergency situations under lack of resources. The approach is motivated by the COVID-19 pandemic in which many Heath National Systems were not able to satisfy the demand of ventilators, sanitary individual protection equipment or different human resources. Our tool is based in two main principles: (1) Part of the stock of equipment at a unit that is not needed (in near future) could be shared to other units; and (2) extra stock to be shared among the units in a region can be efficiently distributed taking into account the demand of the units. The decisions are taken with the aim of minimizing certain measures of the non-covered demand in a region where units are structured in a given network. The mathematical programming models that we provide are stochastic and multiperiod with different robust objective functions. Since the proposed models are computationally hard to solve, we provide a divide-et-conquer math-heuristic approach. We report the results of applying our approach to the COVID-19 case in different regions of Spain, highlighting some interesting conclusions of our analysis, such as the great increase of treated patients if the proposed redistribution tool is applied.","PeriodicalId":75228,"journal":{"name":"Top (Berlin, Germany)","volume":"31 2","pages":"355-390"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9610377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0