Optimization Models Using Fuzzy Sets and Possibility Theory

S. A. Orlovski

ISBN 10: 9401082200 / ISBN 13: 9789401082204

Published by Springer Netherlands, Springer Netherlands Apr 2014, 2014

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Optimization is of central concern to a number of discip lines. Operations Research and Decision Theory are often consi dered to be identical with optimizationo But also in other areas such as engineering design, regional policy, logistics and many others, the search for optimal solutions is one of the prime goals. The methods and models which have been used over the last decades in these areas have primarily been 'hard' or 'crisp', i. e. the solutions were considered to be either fea sible or unfeasible, either above a certain aspiration level or below. This dichotomous structure of methods very often forced the modeller to approximate real problem situations of the more-or-less type by yes-or-no-type models, the solutions of which might turn out not to be the solutions to the real prob lems. This is particularly true if the problem under considera tion includes vaguely defined relationships, human evaluations, uncertainty due to inconsistent or incomplete evidence, if na tural language has to be modelled or if state variables can only be described approximately. Until recently, everything which was not known with cer tainty, i. e. which was not known to be either true or false or which was not known to either happen with certainty or to be impossible to occur, was modelled by means of probabilitieso This holds in particular for uncertainties concerning the oc currence of events.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 480 pp. Englisch.

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Bibliographic Details

• Title: Optimization Models Using Fuzzy Sets and Possibility Theory
• Publisher: Springer Netherlands, Springer Netherlands Apr 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 9401082200
• ISBN 13: 9789401082204
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 26D millimeters 229 millimeters width by 152 millimeters height by 26 millimeters depth
• Weight: 689 grams

About this title

Synopsis

Optimization is of central concern to a number of discip­ lines. Operations Research and Decision Theory are often consi­ dered to be identical with optimizationo But also in other areas such as engineering design, regional policy, logistics and many others, the search for optimal solutions is one of the prime goals. The methods and models which have been used over the last decades in these areas have primarily been "hard" or "crisp", i. e. the solutions were considered to be either fea­ sible or unfeasible, either above a certain aspiration level or below. This dichotomous structure of methods very often forced the modeller to approximate real problem situations of the more-or-less type by yes-or-no-type models, the solutions of which might turn out not to be the solutions to the real prob­ lems. This is particularly true if the problem under considera­ tion includes vaguely defined relationships, human evaluations, uncertainty due to inconsistent or incomplete evidence, if na­ tural language has to be modelled or if state variables can only be described approximately. Until recently, everything which was not known with cer­ tainty, i. e. which was not known to be either true or false or which was not known to either happen with certainty or to be impossible to occur, was modelled by means of probabilitieso This holds in particular for uncertainties concerning the oc­ currence of events.