Fuzzy Sets and Interactive Multiobjective Optimization [electronic resource] /

The main characteristics of the real-world decision-making problems facing humans today are multidimensional and have multiple objectives including eco­ nomic, environmental, social, and technical ones. Hence, it seems natural that the consideration of many objectives in the actual decision-making process re­ quires multiobjective approaches rather than single-objective. One ofthe major systems-analytic multiobjective approaches to decision-making under constraints is multiobjective optimization as a generalization of traditional single-objective optimization. Although multiobjective optimization problems differ from single­ objective optimization problems only in the plurality of objective functions, it is significant to realize that multiple objectives are often noncom mensurable and conflict with each other in multiobjective optimization problems. With this ob­ servation, in multiobjective optimization, the notion of Pareto optimality or effi­ ciency has been introduced instead of the optimality concept for single-objective optimization. However, decisions with Pareto optimality or efficiency are not uniquely determined; the final decision must be selected from among the set of Pareto optimal or efficient solutions. Therefore, the question is, how does one find the preferred point as a compromise or satisficing solution with rational pro­ cedure? This is the starting point of multiobjective optimization. To be more specific, the aim is to determine how one derives a compromise or satisficing so­ lution of a decision maker (DM), which well represents the subjective judgments, from a Pareto optimal or an efficient solution set.

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Main Authors: Sakawa, Masatoshi. author., SpringerLink (Online service)
Format: Texto biblioteca
Language:eng
Published: Boston, MA : Springer US : Imprint: Springer, 1993
Subjects:Mathematics., Computer science., Mathematics, general., Computer Science, general.,
Online Access:http://dx.doi.org/10.1007/978-1-4899-1633-4
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record_format koha
institution COLPOS
collection Koha
country México
countrycode MX
component Bibliográfico
access En linea
En linea
databasecode cat-colpos
tag biblioteca
region America del Norte
libraryname Departamento de documentación y biblioteca de COLPOS
language eng
topic Mathematics.
Computer science.
Mathematics.
Mathematics, general.
Computer Science, general.
Mathematics.
Computer science.
Mathematics.
Mathematics, general.
Computer Science, general.
spellingShingle Mathematics.
Computer science.
Mathematics.
Mathematics, general.
Computer Science, general.
Mathematics.
Computer science.
Mathematics.
Mathematics, general.
Computer Science, general.
Sakawa, Masatoshi. author.
SpringerLink (Online service)
Fuzzy Sets and Interactive Multiobjective Optimization [electronic resource] /
description The main characteristics of the real-world decision-making problems facing humans today are multidimensional and have multiple objectives including eco­ nomic, environmental, social, and technical ones. Hence, it seems natural that the consideration of many objectives in the actual decision-making process re­ quires multiobjective approaches rather than single-objective. One ofthe major systems-analytic multiobjective approaches to decision-making under constraints is multiobjective optimization as a generalization of traditional single-objective optimization. Although multiobjective optimization problems differ from single­ objective optimization problems only in the plurality of objective functions, it is significant to realize that multiple objectives are often noncom mensurable and conflict with each other in multiobjective optimization problems. With this ob­ servation, in multiobjective optimization, the notion of Pareto optimality or effi­ ciency has been introduced instead of the optimality concept for single-objective optimization. However, decisions with Pareto optimality or efficiency are not uniquely determined; the final decision must be selected from among the set of Pareto optimal or efficient solutions. Therefore, the question is, how does one find the preferred point as a compromise or satisficing solution with rational pro­ cedure? This is the starting point of multiobjective optimization. To be more specific, the aim is to determine how one derives a compromise or satisficing so­ lution of a decision maker (DM), which well represents the subjective judgments, from a Pareto optimal or an efficient solution set.
format Texto
topic_facet Mathematics.
Computer science.
Mathematics.
Mathematics, general.
Computer Science, general.
author Sakawa, Masatoshi. author.
SpringerLink (Online service)
author_facet Sakawa, Masatoshi. author.
SpringerLink (Online service)
author_sort Sakawa, Masatoshi. author.
title Fuzzy Sets and Interactive Multiobjective Optimization [electronic resource] /
title_short Fuzzy Sets and Interactive Multiobjective Optimization [electronic resource] /
title_full Fuzzy Sets and Interactive Multiobjective Optimization [electronic resource] /
title_fullStr Fuzzy Sets and Interactive Multiobjective Optimization [electronic resource] /
title_full_unstemmed Fuzzy Sets and Interactive Multiobjective Optimization [electronic resource] /
title_sort fuzzy sets and interactive multiobjective optimization [electronic resource] /
publisher Boston, MA : Springer US : Imprint: Springer,
publishDate 1993
url http://dx.doi.org/10.1007/978-1-4899-1633-4
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spelling KOHA-OAI-TEST:1746112018-07-30T22:52:25ZFuzzy Sets and Interactive Multiobjective Optimization [electronic resource] / Sakawa, Masatoshi. author. SpringerLink (Online service) textBoston, MA : Springer US : Imprint: Springer,1993.engThe main characteristics of the real-world decision-making problems facing humans today are multidimensional and have multiple objectives including eco­ nomic, environmental, social, and technical ones. Hence, it seems natural that the consideration of many objectives in the actual decision-making process re­ quires multiobjective approaches rather than single-objective. One ofthe major systems-analytic multiobjective approaches to decision-making under constraints is multiobjective optimization as a generalization of traditional single-objective optimization. Although multiobjective optimization problems differ from single­ objective optimization problems only in the plurality of objective functions, it is significant to realize that multiple objectives are often noncom mensurable and conflict with each other in multiobjective optimization problems. With this ob­ servation, in multiobjective optimization, the notion of Pareto optimality or effi­ ciency has been introduced instead of the optimality concept for single-objective optimization. However, decisions with Pareto optimality or efficiency are not uniquely determined; the final decision must be selected from among the set of Pareto optimal or efficient solutions. Therefore, the question is, how does one find the preferred point as a compromise or satisficing solution with rational pro­ cedure? This is the starting point of multiobjective optimization. To be more specific, the aim is to determine how one derives a compromise or satisficing so­ lution of a decision maker (DM), which well represents the subjective judgments, from a Pareto optimal or an efficient solution set.1. Introduction -- 2. Fundamentals of Fuzzy Set Theory -- 3. Fuzzy Linear Programming -- 4. Fuzzy Nonlinear Programming -- 5. Interactive Multiobjective Linear Programming with Fuzzy Parameters -- 6. Interactive Multiobjective Nonlinear Programming with Fuzzy Parameters -- 7. Interactive Computer Programs -- 8. Some Applications -- 9. Further Research Directions -- Appendix: Hyperplane Methods and Trade-Offs -- A.1 Hyperplane problems -- A.2 Trade-offs -- References.The main characteristics of the real-world decision-making problems facing humans today are multidimensional and have multiple objectives including eco­ nomic, environmental, social, and technical ones. Hence, it seems natural that the consideration of many objectives in the actual decision-making process re­ quires multiobjective approaches rather than single-objective. One ofthe major systems-analytic multiobjective approaches to decision-making under constraints is multiobjective optimization as a generalization of traditional single-objective optimization. Although multiobjective optimization problems differ from single­ objective optimization problems only in the plurality of objective functions, it is significant to realize that multiple objectives are often noncom mensurable and conflict with each other in multiobjective optimization problems. With this ob­ servation, in multiobjective optimization, the notion of Pareto optimality or effi­ ciency has been introduced instead of the optimality concept for single-objective optimization. However, decisions with Pareto optimality or efficiency are not uniquely determined; the final decision must be selected from among the set of Pareto optimal or efficient solutions. Therefore, the question is, how does one find the preferred point as a compromise or satisficing solution with rational pro­ cedure? This is the starting point of multiobjective optimization. To be more specific, the aim is to determine how one derives a compromise or satisficing so­ lution of a decision maker (DM), which well represents the subjective judgments, from a Pareto optimal or an efficient solution set.Mathematics.Computer science.Mathematics.Mathematics, general.Computer Science, general.Springer eBookshttp://dx.doi.org/10.1007/978-1-4899-1633-4URN:ISBN:9781489916334