Pure and mixed lexicographic-paretian many-objective optimization: state of the art

Lai, Leonardo; Fiaschi, Lorenzo; Cococcioni, Marco; Deb, Kalyanmoy

doi:10.1007/s11047-022-09911-4

Cited by 4 publications

References 54 publications

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Multi-objective Lexicographic Mixed-Integer Linear Programming: An Infinity Computer Approach

Cococcioni

Cudazzo

Pappalardo

et al. 2022

Emergence, Complexity and Computation

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translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Multi-objective Lexicographic Mixed-Integer Linear Programming: An Infinity Computer Approach

Cococcioni

Cudazzo

Pappalardo

et al. 2022

Emergence, Complexity and Computation

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On efficiency and the Jain’s fairness index in integer assignment problems

Rezaeinia,

Góez,

Guajardo

2023

Comput Manag Sci

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Given two sets of objects, the integer assignment problem consists of assigning objects of one set to objects in the other set. Traditionally, the goal of this problem is to find an assignment that minimizes or maximizes a measure of efficiency, such as maximization of utility or minimization of cost. Lately, the interest in incorporating a measure of fairness in addition to efficiency has gained importance. This paper studies how to incorporate these two criteria in an integer assignment, using the Jain’s index as a measure of fairness. The original formulation of the assignment problem with this index involves a non-concave function, which renders a non-linear non-convex problem, usually hard to solve. To this aim, we develop two reformulations, where one is based on a quadratic objective function and the other one is based on integer second-order cone programming. We explore the performance of these reformulations in instances of real-world data derived from an application of assigning personnel to projects, and also in instances of randomly generated data. In terms of solution quality, all formulations prove to be effective in finding solutions capturing both efficiency and fairness criteria, with some slight differences depending on the type of instance. In terms of solving time, however, the performances of the formulations differ considerably. In particular, the integer quadratic approach proves to be much faster in finding optimal solutions.

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Cost and response time optimization of edge architectures

Entrialgo,

Moreno-Vozmediano,

Díaz

2024

Cluster Comput

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The cloud service provider market has recently expanded its offerings by providing edge as a service. This involves offering resources equivalent to those already available in the cloud, but through data centers located closer to the end user, with the goal of improving service latencies. Application providers face the challenge of selecting appropriate resources, both from the edge and cloud, to deploy their applications in a way that minimizes deployment costs while satisfying latency requirements. This paper presents Edarop (EDge ARchitecture OPtimization), an innovative orchestration mechanism for the optimal allocation of virtual machines in geographically distributed edge and cloud infrastructures. Edarop is capable of handling different edge and cloud vendors, each offering various types of VMs in different regions, with different prices, and network latencies. It also supports multiple simultaneous applications with different latency requirements and load profiles. Edarop employs Integer Linear Programming (ILP) to ensure the globally optimal solution within a reasonable time frame for the considered use cases. Several variants of the mechanism are provided, depending on whether the objective is to minimize cost, response times, or both. These variants are compared to each other and to alternative approaches, with the results showing that, unlike other methods, Edarop consistently respects latency constraints while minimizing the proposed objectives.

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Pure and mixed lexicographic-paretian many-objective optimization: state of the art

Cited by 4 publications

References 54 publications

Multi-objective Lexicographic Mixed-Integer Linear Programming: An Infinity Computer Approach

Multi-objective Lexicographic Mixed-Integer Linear Programming: An Infinity Computer Approach

On efficiency and the Jain’s fairness index in integer assignment problems

Cost and response time optimization of edge architectures

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