On the design of truthful mechanisms for the capacitated facility location problem with two and more facilities

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Gennaro Auricchio , Zihe Wang , Jie Zhang
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Abstract

In this paper, we explore the Mechanism Design aspects of the m-Capacitated Facility Location Problem (m-CFLP) on a line, focusing on two frameworks. In the first framework, the number of facilities is arbitrary, all facilities share the same capacity, and the number of agents matches the total capacity of the facilities. In the second framework, we need to locate two facilities, each with a capacity equal to at least half the number of agents. For both frameworks, we propose truthful mechanisms with bounded approximation ratios in terms of Social Cost (SC) and Maximum Cost (MC). When m>2, our results stand in contrast to the impossibility results known for the classical m-Facility Location Problem, where capacity constraints are absent. Moreover, all the proposed mechanisms are optimal with respect to MC and either optimal or near-optimal with respect to the SC among anonymous mechanisms. We then establish lower bounds on the approximation ratios that any truthful and deterministic mechanism achieves with respect to SC and MC for both frameworks. Lastly, we run several numerical experiments to empirically evaluate the performances of our mechanisms with respect to the SC or the MC. Our empirical analysis shows that our proposed mechanisms outperform all previously proposed mechanisms applicable in this setting.
两个及两个以上可容设施选址问题的真实机制设计
在本文中,我们探讨了m-Capacitated设施选址问题(m-CFLP)在一条线上的机制设计方面,重点是两个框架。在第一个框架中,设施的数量是任意的,所有设施共享相同的容量,代理的数量与设施的总容量相匹配。在第二个框架中,我们需要找到两个设施,每个设施的容量至少等于代理数量的一半。对于这两个框架,我们提出了基于社会成本(SC)和最大成本(MC)的有界近似比的真实机制。当m>;2时,我们的结果与不存在容量约束的经典m-设施选址问题的不可能结果形成对比。此外,所有提出的机制都是最优的MC和最优或接近最优的SC在匿名机制。然后,我们建立了关于两个框架的SC和MC的任何真实和确定性机制所达到的近似比率的下界。最后,我们进行了几个数值实验,以经验性地评估我们的机制相对于SC或MC的性能。我们的实证分析表明,我们提出的机制优于所有先前提出的适用于此设置的机制。
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
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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