Pavlos Eirinakis

Hyperarc Consistency for the Stable Admissions Problem

This paper presents an algorithm achieving hyperarc consistency for the stable admissions problem and discusses computational results.The multiplication of very high resolution (spatial or spectral) remote sensing images appears to be an opportunity to identify objects in urban and periurban areas. The classification methods applied in the object-oriented image analysis approach could be based on the use of domain knowledge. A major issue in these approaches is domain knowledge formalization and exploitation. In this paper, we propose a recognition method based on an ontology which has been developed by experts of the domain. In order to give objects a semantic meaning, we have developed a matching process between an object and the concepts of the ontology. Experiments are made on a Quickbird image. The quality of the results shows the effectiveness of the proposed method.

Pareto optimality in many-to-many matching problems

Consider a many-to-many matching market that involves two finite disjoint sets, a set A of applicants and a set C of courses. Each applicant has preferences on the different sets of courses she can attend, while each course has a quota of applicants that it can admit. In this paper, we examine Pareto optimal matchings (briefly POM) in the context of such markets, that can also incorporate additional constraints, e.g., each course bearing some cost and each applicant having a limited budget available. We provide necessary and sufficient conditions for a many-to-many matching to be Pareto optimal and show that checking whether a given matching is Pareto optimal can be accomplished in O ( ? A ? 2 ? ? C ? 2 ) time. Moreover, we provide a generalized version of serial dictatorship, which can be used to obtain any many-to-many POM. We also study some structural questions related to POM. We show that, unlike in the one-to-one case, finding a maximum cardinality POM is NP-hard for many-to-many markets.

Finding All Stable Pairs and Solutions to the Many-to-Many Stable Matching Problem

The many-to-many stable matching problem (MM), defined in the context of a job market, asks for an assignment of workers to firms satisfying the quota of each agent and being stable, pairwise or setwise, with respect to given preference lists or relations. In this paper, we propose a time-optimal algorithm that identifies all stable worker--firm pairs and all stable assignments under pairwise stability, individual preferences, and the max-min criterion. We revisit the poset graph of rotations to obtain an optimal algorithm for enumerating all solutions to the MM and an improved algorithm finding the minimum-weight one. Furthermore, we establish the applicability of all aforementioned algorithms under more complex preference and stability criteria. In a constraint programming context, we introduce a constraint that models the MM and an encoding of the MM as a constraint satisfaction problem. Finally, we provide a series of computational results, including the case where side constraints are imposed.

Polyhedral Aspects of Stable Marriage

In the setting of the stable matching (SM) problem, it has been observed that some of the man-woman pairs cannot be removed although they participate in no stable matching, since such a removal would alter the set of solutions. These pairs are yet to be identified. Likewise (and despite the sizeable literature), some of the fundamental characteristics of the SM polytope (e.g., its dimension, its facets, etc.) have not been established. In the current work, we show that these two seemingly distant open issues are closely related. More specifically, we identify the pairs with the above-mentioned property and present a polynomial algorithm for producing a set of minimal preference lists. We utilize this result in the context of two different representations of the SM structure (rotation-poset graph and algebraic formulation) and derive the dimension of the SM polytope to obtain all alternative minimal linear descriptions.

Pareto Optimal Matchings in Many-to-Many Markets with Ties

We consider Pareto optimal matchings (POMs) in a many-to-many market of applicants and courses where applicants have preferences, which may include ties, over individual courses and lexicographic preferences over sets of courses. Since this is the most general setting examined so far in the literature, our work unifies and generalizes several known results. Specifically, we characterize POMs and introduce the Generalized Serial Dictatorship Mechanism with Ties (GSDT) that effectively handles ties via properties of network flows. We show that GSDT can generate all POMs using different priority orderings over the applicants, but it satisfies truthfulness only for certain such orderings. This shortcoming is not specific to our mechanism; we show that any mechanism generating all POMs in our setting is prone to strategic manipulation. This is in contrast to the one-to-one case (with or without ties), for which truthful mechanisms generating all POMs do exist.

Blockers and antiblockers of stable matchings

An implicit linear description of the stable matching polytope is provided in terms of the blocker and antiblocker sets of constraints of the matroid-kernel polytope. The explicit identification of both these sets is based on a partition of the stable pairs in which each agent participates. Here, we expose the relation of such a partition to rotations. We provide a time-optimal algorithm for obtaining such a partition and establish some new related results; most importantly, that this partition is unique.

A Proposal of Decentralised Architecture for Optimised Operations in Manufacturing Ecosystem Collaboration

This paper describes an innovative approach to adopt the next-generation manufacturing paradigm based on flexible production units and eco-systems that can be quickly reprogrammed to provide fast time-to-market responses to global consumer demand, address mass-customisation needs and bring life to innovative products. The approach utilises the capabilities offered by digitalisation to facilitate (i) in-depth (self-) monitoring of machines and processes, (ii) decision support and decentralised (self-) adjustment of production, (iii) effective collaboration of the different IoT-connected machines with tools, services and actors (iv) seamless communication of information and decisions from and to the plant floor and (v) efficient interaction with value chain partners. The paper presents the conceptual architecture under development to support those functionalities for two specific domains in manufacturing.

Erratum to: Analyzing restricted fragments of the theory of linear arithmetic

AbstractErratum to: Ann Math Artif Intell (2017) 79:245-266DOI 10.1007/s10472-016-9525-7 Owing to an error in the production process, the following article has been published incorrectly online. The article including the illustrations is presented hereafter.

Fast Algorithms for the Undirected Negative Cost Cycle Detection Problem

The undirected negative cost cycle detection (UNCCD) problem is concerned with checking whether an undirected, weighted graph contains a negative cost cycle. Known approaches for solving this problem involve reducing it to either the minimum weight