使用根连接树的影响图的风险规避决策策略

IF 0.8 4区 管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Olli Herrala, Topias Terho, Fabricio Oliveira
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引用次数: 0

摘要

本文介绍了如何将基于渐进式根连接树表示的影响图的混合整数规划(MIP)公式扩展为包含更一般的建模特征,例如风险考虑和特定问题的约束。我们提出了两种算法,通过对潜在的影响图或相关的渐进根连接树表示进行有针对性的修改来实现我们的重新表述。我们提出了计算实验,突出了我们的重新配方的优越计算性能,而不是替代的最先进的MIP公式的影响图,默认情况下,可以容纳这些建模特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Operations Research Letters
Operations Research Letters 管理科学-运筹学与管理科学
CiteScore
2.10
自引率
9.10%
发文量
111
审稿时长
83 days
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.
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