Fair distribution of delivery orders

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hadi Hosseini , Shivika Narang , Tomasz Wąs
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引用次数: 0

Abstract

We initiate the study of fair distribution of delivery tasks among a set of agents wherein delivery jobs are placed along the vertices of a graph. Our goal is to fairly distribute delivery costs (distance traveled to complete the deliveries) among a fixed set of agents while satisfying some desirable notions of economic efficiency. We adopt well-established fairness concepts—such as envy-freeness up to one item (EF1) and minimax share (MMS)—to our setting and show that fairness is often incompatible with the efficiency notion of social optimality. We then characterize instances that admit fair and socially optimal solutions by exploiting graph structures. We further show that achieving fairness along with Pareto optimality is computationally intractable. We complement this by designing an XP algorithm (parameterized by the number of agents) for finding MMS and Pareto optimal solutions on every tree instance, and show that the same algorithm can be modified to find efficient solutions along with EF1, when such solutions exist. The latter crucially relies on an intriguing result that in our setting EF1 and Pareto optimality jointly imply MMS. We conclude by theoretically and experimentally analyzing the price of fairness.
公平分配交货订单
我们开始研究配送任务在一组代理之间的公平分配,其中配送任务沿着图的顶点放置。我们的目标是在一组固定的代理中公平分配配送成本(完成配送的距离),同时满足一些理想的经济效率概念。我们采用了公认的公平概念——例如最多一项嫉妒(EF1)和最小最大份额(MMS)——来进行我们的设置,并表明公平通常与社会最优的效率概念不相容。然后,我们通过利用图结构来描述承认公平和社会最优解的实例。我们进一步表明,实现公平与帕累托最优是计算难以处理的。我们通过设计一个XP算法(由代理数量参数化)来补充这一点,用于在每个树实例上寻找MMS和Pareto最优解,并表明当这样的解存在时,相同的算法可以被修改以找到与EF1一起的有效解。后者主要依赖于一个有趣的结果,即在我们的设置中,EF1和帕累托最优联合暗示MMS。本文通过对公平价格的理论分析和实验分析得出结论。
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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