Performance Prediction and Analytics of Fuzzy, Reliability and Queuing Models : Theory and Applications

Deep, Kusum and Jain, Madhu and Salhi, Said, eds. (2019) Performance Prediction and Analytics of Fuzzy, Reliability and Queuing Models : Theory and Applications. Asset Analytics . Springer, Singapore, Singapore, 250 pp. ISBN 978-981-1308-56-7. E-ISBN 978-981-1308-57-4. (doi:https://doi.org/10.1007/978-981-13-0857-4) (Full text available)

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
Uncontrolled keywords: analytics, fuzzy logic, reliability, performance, prediction, queueing
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Faculties > Social Sciences > Kent Business School > Management Science
Central Services > Research Services
Faculties > Social Sciences > Kent Business School > Centre for Logistics and Heuristic Organisation (CLHO)
Depositing User: Said Salhi
Date Deposited: 26 Jul 2018 10:26 UTC
Last Modified: 03 Apr 2019 16:10 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/68436 (The current URI for this page, for reference purposes)
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