Envy-free house allocation with minimum subsidy

IF 0.8 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2024-03-02 DOI:10.1016/j.orl.2024.107103

Davin Choo , Yan Hao Ling , Warut Suksompong , Nicholas Teh , Jian Zhang

引用次数: 0

Abstract

House allocation refers to the problem where m houses are to be allocated to n agents so that each agent receives one house. Since an envy-free house allocation does not always exist, we consider finding such an allocation in the presence of subsidy. We show that computing an envy-free allocation with minimum subsidy is NP-hard in general, but can be done efficiently if m differs from n by an additive constant or if the agents have identical utilities.

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最低补贴下的无羡住房分配

房屋分配指的是这样一个问题：将 m 所房屋分配给 n 个代理人，使每个代理人都能得到一所房屋。由于不令人嫉妒的房屋分配并不总是存在，因此我们考虑在有补贴的情况下找到这样一种分配。我们的研究表明，计算补贴最小的无妒忌分配一般来说是 NP 难的，但如果 m 与 n 相差一个加常数，或者代理人有相同的效用，则可以高效地完成计算。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.