Deep, Kusum and Jain, Madhu and Salhi, Said, eds. (2019) Performance Prediction and Analytics of Fuzzy, Reliability and Queuing Models : Theory and Applications. First edition. Asset Analytics . Springer, Singapore, Singapore, 250 pp. ISBN 978-981-13-0856-7. E-ISBN 978-981-13-0857-4. (doi:10.1007/978-981-13-0857-4) (KAR id:68436)
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Official URL: http://dx.doi.org/10.1007/978-981-13-0857-4. |
Abstract
This book presents the latest developments and breakthroughs in fuzzy theory and
performance prediction of queuing and reliability models by using the stochastic modeling and
optimization theory. The main focus is on analytics that use fuzzy logic, queuing and reliability
theory for the performance prediction and optimal design of real-time engineering systems
including call centers, telecommunication, manufacturing, service organizations, etc. For the dayto-
day as well as industrial queuing situations and reliability prediction of machining parts
embedded in computer, communication and manufacturing systems, the book assesses various
measures of performance and effectiveness that can provide valuable insights and help arrive
at the best decisions with regard to service and engineering systems. In twenty chapters, the
book presents both theoretical developments and applications of the fuzzy logic, reliability and
queuing models in a diverse range of scenarios. The topics discussed will be of interest to
researchers, educators and undergraduate students in the fields of Engineering, Business
Management, and the Mathematical Sciences.
Item Type: | Edited book |
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DOI/Identification number: | 10.1007/978-981-13-0857-4 |
Uncontrolled keywords: | analytics, fuzzy logic, reliability, performance, prediction, queueing |
Subjects: | H Social Sciences > H Social Sciences (General) |
Divisions: |
Central Services > Research and Innovation Services Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Depositing User: | Said Salhi |
Date Deposited: | 26 Jul 2018 10:26 UTC |
Last Modified: | 05 Nov 2024 12:29 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/68436 (The current URI for this page, for reference purposes) |
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