Data-driven Probabilistic Models in Decision Making Process
Description

PART A: Service Engineering
Service sector is central in the life of post-industrial societies - more than 70% of the Gross National Product in most developed countries is due to this sector. Important examples are healthcare systems (hospitals), financial services (banks) and telephone and internet services. In concert with this state of affairs, there exists a growing demand for high-quality multi-disciplinary research in the field of services, as well as for a significant number of Service Engineers, namely scientifically-educated specialists that are capable of designing service systems, as well as solving multi-faceted problems that arise in their practice. The course will provide a framework for modeling service systems and techniques that are used to design, analyze, and operate service systems. Our teaching approach is data oriented: examples from various service sectors are presented at lectures and homework assignments, with the call center industry being the central application area. In this course, a service system is viewed as a stochastic network. Thus, the main theoretical framework is queuing theory, which primarily involves a large class of stochastic models. However, the subject matter is highly multi-disciplinary; hence alternative frameworks are useful as well, including ones from Statistics, Psychology, and Marketing.

PART B: Engineering Reliability
The mathematical theory of reliability has grown out of the continually increasing demands of technology. Reliability is the probability of a system performing its purpose adequately for a period of time intended under operating conditions encountered. The teaching of this part of the course concentrates on coherent system reliability, failure data analysis and maintenance policies. It will be developed the use of probability theory for the study of reliability and life time of the systems, via appropriate probabilistic models and statistical methods for studying reliability data.

Division: Statistics, Probability and Operational Research
Recommended Literature:

Program of Studies:
Postgraduate - MCDA
Semester: B
ECTS: 7.5
Hours per week (Lec/Tut/L): 3/0/0
Code: MCDA103
Erasmus students: No




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