Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis : Recent Advances, de Almeida, Adiel Teixeira (ed.); Ekenberg, Love (ed.); Scarf, Philip (ed.); Zio, Enrico (ed.); Zuo, Ming J. (ed.), Springer

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Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis : Recent Advances

Adiel Teixeira de Almeida (ed.), Love Ekenberg (ed.), Philip Scarf (ed.), Enrico Zio (ed.), Ming J. Zuo (ed.)

129,90 €

Springer
Sivumäärä: 508 sivua
Asu: Kovakantinen kirja
Julkaisuvuosi: 2022, 29.06.2022 (lisätietoa)
Kieli: Englanti
Tuotesarja: International Series in Operations Research & Management Science 321

This book considers a broad range of areas from decision making methods applied in the contexts of Risk, Reliability and Maintenance (RRM). Intended primarily as an update of the 2015 book Multicriteria and Multiobjective Models for Risk, Reliability and Maintenance Decision Analysis, this edited work provides an integration of applied probability and decision making. Within applied probability, it primarily includes decision analysis and reliability theory, amongst other topics closely related to risk analysis and maintenance.

In decision making, it includes multicriteria decision making/aiding (MCDM/A) methods and optimization models. Within MCDM, in addition to decision analysis, some of the topics related to mathematical programming areas are considered, such as multiobjective linear programming, multiobjective nonlinear programming, game theory and negotiations, and multiobjective optimization. Methods related to these topics have been applied to the context of RRM. In MCDA, several other methods are considered, such as outranking methods, rough sets and constructive approaches.

The book addresses an innovative treatment of decision making in RRM, improving the integration of fundamental concepts from both areas of RRM and decision making. This is accomplished by presenting current research developments in decision making on RRM. Some pitfalls of decision models on practical applications on RRM are discussed and new approaches for overcoming those drawbacks are presented.

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