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Weibull Decision Support Systems in Maintenance

100%
Aboura K., Agbinya J. I., Eskandarian A.
Organizacija
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2014
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vol. 47
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issue 2
81-89
EN
Background: The Weibull distribution is one of the most important lifetime distributions in applied statistics. Weibull analysis is the leading method in the world for fitting and analyzing lifetime data. We discuss one of the earliest decision support system for the assessment of a distribution for the parameters of the Weibull reliability model using expert information. We then present a different approach to assess the parameters distribution. Objectives: The studies mentioned in this paper aimed to construct a distribution of the parameters of the Weibull reliability model and apply it in the domain of Maintenance Optimization. Method: The parameters of the Weibull reliability model are considered random variables and a distribution for the parameters is assessed using informed judgment in the form of reliability estimates from vendor information, engineering knowledge or experience in the field. Results: The results are the development of modern maintenance optimization models that can be embodied in decision support systems. Conclusion: While the information management part is important in the building of maintenance optimization decision systems, the accuracy of the mathematical and statistical algorithms determines the level of success of the maintenance solution.
2

The need for simulation in complex industrial systems

100%
Aboura K., Kljajić M., Eskandarian A.
Organizacija
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2012
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vol. 45
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issue 5
219-227
EN
We discuss the concept of simulation and its application in the resolution of problems in complex industrial systems. Most problems of serious scale, be it an inventory problem, a production and distribution problem, a management of resources or process improvement, all real world problems require a mix of generic, data algorithmic and Ad-hoc solutions making the best of available information. We describe two projects in which analytical solutions were applied or contemplated. The first case study uses linear programming in the optimal allocation of advertising resources by a major internet service provider. The second study, in a series of projects, analyses options for the expansion of the production and distribution network of mining products, as part of a sensitive strategic business review. Using the examples, we make the case for the need of simulation in complex industrial problems where analytical solutions may be attempted but where the size and complexity of the problem forces a Monte Carlo approach.
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