Fair Optimization and Networks: A Survey
Optimization models related to designing and operating complex systems are mainly focused on some efficiency metrics such as response time, queue length, throughput, and cost. However, in systems which serve many entities there is also a need for respecting fairness: each system entity ought to be p...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/612018 |
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| _version_ | 1850218121314435072 |
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| author | Wlodzimierz Ogryczak Hanan Luss Michał Pióro Dritan Nace Artur Tomaszewski |
| author_facet | Wlodzimierz Ogryczak Hanan Luss Michał Pióro Dritan Nace Artur Tomaszewski |
| author_sort | Wlodzimierz Ogryczak |
| collection | DOAJ |
| description | Optimization models related to designing and operating complex systems are mainly focused on some efficiency metrics such as response time, queue length, throughput, and cost. However, in systems which serve many entities there is also a need for respecting fairness: each system entity ought to be provided with an adequate share of the system’s services. Still, due to system operations-dependant constraints, fair treatment of the entities does not directly imply that each of them is assigned equal amount of the services. That leads to concepts of fair optimization expressed by the equitable models that represent inequality averse optimization rather than strict inequality minimization; a particular widely applied example of that concept is the so-called lexicographic maximin optimization (max-min fairness). The fair optimization methodology delivers a variety of techniques to generate fair and efficient solutions. This paper reviews fair optimization models and methods applied to systems that are based on some kind of network of connections and dependencies, especially, fair optimization methods for the location problems and for the resource allocation problems in communication networks. |
| format | Article |
| id | doaj-art-40fc0ec719f94b7ea9bd2930611b64cf |
| institution | OA Journals |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-40fc0ec719f94b7ea9bd2930611b64cf2025-08-20T02:07:52ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/612018612018Fair Optimization and Networks: A SurveyWlodzimierz Ogryczak0Hanan Luss1Michał Pióro2Dritan Nace3Artur Tomaszewski4Institute of Control and Computation Engineering, Warsaw University of Technology, 00-665 Warsaw, PolandDepartment of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USADepartment of Electrical and Information Technology, Lund University, 22100 Lund, SwedenLaboratoire Heudiasyc, Université de Technologie de Compiègne, 60203 Compiègne, FranceInstitute of Telecommunications, Warsaw University of Technology, 00-665 Warsaw, PolandOptimization models related to designing and operating complex systems are mainly focused on some efficiency metrics such as response time, queue length, throughput, and cost. However, in systems which serve many entities there is also a need for respecting fairness: each system entity ought to be provided with an adequate share of the system’s services. Still, due to system operations-dependant constraints, fair treatment of the entities does not directly imply that each of them is assigned equal amount of the services. That leads to concepts of fair optimization expressed by the equitable models that represent inequality averse optimization rather than strict inequality minimization; a particular widely applied example of that concept is the so-called lexicographic maximin optimization (max-min fairness). The fair optimization methodology delivers a variety of techniques to generate fair and efficient solutions. This paper reviews fair optimization models and methods applied to systems that are based on some kind of network of connections and dependencies, especially, fair optimization methods for the location problems and for the resource allocation problems in communication networks.http://dx.doi.org/10.1155/2014/612018 |
| spellingShingle | Wlodzimierz Ogryczak Hanan Luss Michał Pióro Dritan Nace Artur Tomaszewski Fair Optimization and Networks: A Survey Journal of Applied Mathematics |
| title | Fair Optimization and Networks: A Survey |
| title_full | Fair Optimization and Networks: A Survey |
| title_fullStr | Fair Optimization and Networks: A Survey |
| title_full_unstemmed | Fair Optimization and Networks: A Survey |
| title_short | Fair Optimization and Networks: A Survey |
| title_sort | fair optimization and networks a survey |
| url | http://dx.doi.org/10.1155/2014/612018 |
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