{"title":"使用根连接树的影响图的风险规避决策策略","authors":"Olli Herrala, Topias Terho, Fabricio Oliveira","doi":"10.1016/j.orl.2025.107308","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents how a mixed-integer programming (MIP) formulation for influence diagrams that is based on their gradual rooted junction tree representation can be extended to incorporate more general modelling features, such as risk considerations and problem-specific constraints. We propose two algorithms that enable our reformulations by performing targeted modifications either to the underlying influence diagram or to the associated gradual rooted junction tree representation. We present computational experiments highlighting the superior computational performance of our reformulation against an alternative state-of-the-art MIP formulation for influence diagrams that, by default, can accommodate those modelling features.</div></div>","PeriodicalId":54682,"journal":{"name":"Operations Research Letters","volume":"61 ","pages":"Article 107308"},"PeriodicalIF":0.8000,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Risk-averse decision strategies for influence diagrams using rooted junction trees\",\"authors\":\"Olli Herrala, Topias Terho, Fabricio Oliveira\",\"doi\":\"10.1016/j.orl.2025.107308\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper presents how a mixed-integer programming (MIP) formulation for influence diagrams that is based on their gradual rooted junction tree representation can be extended to incorporate more general modelling features, such as risk considerations and problem-specific constraints. We propose two algorithms that enable our reformulations by performing targeted modifications either to the underlying influence diagram or to the associated gradual rooted junction tree representation. We present computational experiments highlighting the superior computational performance of our reformulation against an alternative state-of-the-art MIP formulation for influence diagrams that, by default, can accommodate those modelling features.</div></div>\",\"PeriodicalId\":54682,\"journal\":{\"name\":\"Operations Research Letters\",\"volume\":\"61 \",\"pages\":\"Article 107308\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2025-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Operations Research Letters\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167637725000690\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Operations Research Letters","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167637725000690","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
Risk-averse decision strategies for influence diagrams using rooted junction trees
This paper presents how a mixed-integer programming (MIP) formulation for influence diagrams that is based on their gradual rooted junction tree representation can be extended to incorporate more general modelling features, such as risk considerations and problem-specific constraints. We propose two algorithms that enable our reformulations by performing targeted modifications either to the underlying influence diagram or to the associated gradual rooted junction tree representation. We present computational experiments highlighting the superior computational performance of our reformulation against an alternative state-of-the-art MIP formulation for influence diagrams that, by default, can accommodate those modelling features.
期刊介绍:
Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.