Discrete Optimization with Interval Data : Minmax Regret and Fuzzy Approach

In operations research applications we are often faced with the problem of incomplete or uncertain data. This book considers solving combinatorial optimization problems with imprecise data modeled by intervals and fuzzy intervals. It focuses on some basic and traditional problems, such as minimum sp...

Popoln opis

Shranjeno v:
Bibliografske podrobnosti
Glavni avtor: Kasperski, Adam
Format: Livre numérique
Jezik:Anglais
Izdano: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
Izdaja:1st ed. 2008.
Serija:Studies in Fuzziness and Soft Computing
Online dostop:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Sporočilo: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Discrete optimization with interval data, minmax regret and fuzzy approach, Adam Kasperski, Berlin, Springer, 2008, 1 vol. (xvi, 220 p.), Studies in fuzziness and soft computing, 978-3-540-78483-8
• Discrete Optimization with Interval Data, Texte imprimé, 9783642097201
• Discrete Optimization with Interval Data, Texte imprimé, 9783540849230
Kazalo:
  • Minmax Regret Combinatorial Optimization Problems with Interval Data Problem Formulation Evaluation of Optimality of Solutions and Elements Exact Algorithms Approximation Algorithms Minmax Regret Minimum Selecting Items Minmax Regret Minimum Spanning Tree Minmax Regret Shortest Path Minmax Regret Minimum Assignment Minmax Regret Minimum s???t Cut Fuzzy Combinatorial Optimization Problem Conclusions and Open Problems Minmax Regret Sequencing Problems with Interval Data Problem Formulation Sequencing Problem with Maximum Lateness Criterion Sequencing Problem with Weighted Number of Late Jobs Sequencing Problem with the Total Flow Time Criterion Conclusions and Open Problems Discrete Scenario Representation of Uncertainty