Optimal Mechanisms for Consumer Surplus Maximization

We consider the problem of designing auctions which maximize consumer surplus (i.e., the social welfare minus the payments charged to the buyers). In the consumer surplus maximization problem, a seller with a set of goods faces a set of strategic buyers with private values, each of whom aims to maximize their own individual utility. The seller, in contrast, aims to allocate the goods in a way which maximizes the total buyer utility. The seller must then elicit the values of the buyers in order to decide what goods to award each buyer. The canonical approach in mechanism design to ensure truthful reporting of the private information is to find appropriate prices to charge each buyer in order to align their objective with the objective of the seller. Indeed, there are many celebrated results to this end when the seller's objective is welfare maximization [Clarke, 1971, Groves, 1973, Vickrey, 1961] or revenue maximization [Myerson, 1981]. However, in the case of consumer surplus maximization the picture is less clear -- using high payments to ensure the highest value bidders are served necessarily decreases their surplus utility, but using low payments may lead the seller into serving lower value bidders. Our main result in this paper is a framework for designing mechanisms which maximize consumer surplus. We instantiate our framework in a variety of canonical multi-parameter auction settings (i.e., unit-demand bidders with heterogeneous items, multi-unit auctions, and auctions with divisible goods) and use it to design auctions achieving consumer surplus with optimal approximation guarantees against the total social welfare. Along the way, we answer an open question posed by Hartline and Roughgarden [2008], who, to our knowledge, were the first to study the question of consumer surplus approximation guarantees in single-parameter settings, regarding optimal mechanisms for two bidders

Achieving Proportionality up to the Maximin Item with Indivisible Goods

We study the problem of fairly allocating indivisible goods and focus on the classic fairness notion of proportionality. The indivisibility of the goods is long known to pose highly non-trivial obstacles to achieving fairness, and a very vibrant line of research has aimed to circumvent them using appropriate notions of approximate fairness. Recent work has established that even approximate versions of proportionality (PROPx) may be impossible to achieve even for small instances, while the best known achievable approximations (PROP1) are much weaker. We introduce the notion of proportionality up to the maximin item (PROPm) and show how to reach an allocation satisfying this notion for any instance involving up to five agents with additive valuations. PROPm provides a well-motivated middle-ground between PROP1 and PROPx, while also capturing some elements of the well-studied maximin share (MMS) benchmark: another relaxation of proportionality that has attracted a lot of attention.Comment: Changes to wording throughout and changes to framing of section

Kiri J. E. I. Walch`ile, Argentorati

http://tartu.ester.ee/record=b1880783~S1*es

Kiri tundmatule, Strasbourg

http://tartu.ester.ee/record=b1863654~S1*es

Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul

Deploying deep learning-based applications in specialized domains like the aircraft production industry typically suffers from the training data availability problem. Only a few datasets represent non-everyday objects, situations, and tasks. Recent advantages in research around Vision Foundation Models (VFM) opened a new area of tasks and models with high generalization capabilities in non-semantic and semantic predictions. As recently demonstrated by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a promising direction in tackling the boundaries spanned by data, context, and sensor variety. Although, investigating its application within specific domains is subject to ongoing research. This paper contributes here by surveying applications of the SAM in aircraft production-specific use cases. We include manufacturing, intralogistics, as well as maintenance, repair, and overhaul processes, also representing a variety of other neighboring industrial domains. Besides presenting the various use cases, we further discuss the injection of domain knowledge

Industry 5.0 in aircraft production and MRO: challenges and opportunities

Globally interconnecting machines, processes, and resources driven by exploring and advancing new technologies definedIndustry 4.0 (I4.0), resulting in, e.g., Cyber-Physical Production Systems (CPPS). The aircraft industry particularly struggledwith transforming production and Maintenance, Repair, and Overhaul (MRO) processes, replacing humans with machinesand automating as well as digitalizing significant parts of their value- and non-value-adding activities. However, in theface of current social and environmental challenges, future industries will need to shift from purely technology-driven tovalue-driven, working sustainably with resources, including human capital. Together, these approaches constitute the ideaof Industry 5.0 (I5.0). On the one hand, the aviation industry faces the challenge that even I4.0 concepts and technologiesare not yet fully exploited or implemented. On the other hand, due to the specific characteristics of aircraft production andMRO as well as the environmental impact of the product, a tremendous potential arises regarding placing human well-beingback into the center of adding value and decreasing environmental footprint while building an industry that is resilient andfortified against disruptions of this era. In line with the I5.0 terminology, in this work, we outline the challenges and oppor-tunities of integrating I5.0 principles into the aircraft production and MRO industries, focusing specifically on the scope ofselected use cases. (PDF) Industry 5.0 in aircraft production and MRO: challenges and opportunities. Available from: https://www.researchgate.net/publication/390530552_Industry_50_in_aircraft_production_and_MRO_challenges_and_opportunities [accessed Apr 11 2025]

Synthetische Trainingsdaten für die Produktions- und Instandhaltungsversorgende Logistik von Flugzeugen

Visuelle KI-Applikationen zur Identifikation von Komponenten haben das Potential Fehler in der Intralogistik der Luftfahrt zu vermeiden. Die vorliegende Arbeit stellt ein Verfahren vor, womit die benötigten Bilddaten synthetisch generiert werden können. Hierzu wird eine strukturierte Domänenrandomisierung zur Abbildung der adressierten Domäne entwickelt und durch Untersuchungen zur Eignung verschiedener 3D-Modelle sowie zu verschiedenen Domain Adaption Methoden komplementiert. Die entwickelten Verfahren, damit generierten Daten und trainierte KI-Applikationen werden gegenüber reellen Anwendungsszenarien validier