The main objective of the Μultiple Criteria Decision Analysis (MCDA) is to support decision making in real life complex problems, with conflicting interests and goals, i.e. the “multiple criteria”. The basic feature of MCDA is the appropriate aggregation of different views and dimensions, taking into consideration the necessary preferences and values of the decision makers. Within this course, the overall philosophy and the methodological framework of the MCDA are outlined and the most important quantitative methods, namely the multiple criteria mathematical programming, the multi-attribute utility theory, the outranking methods and the disaggregation methods are presented in detail. Moreover, representative methods and models that can adequately capture and process the qualitative characteristics of the performance of efficient solutions in the problem criteria are also analyzed and practical application examples of MCDA from the fields of technology, management, economy and energy are presented as well.
- Teacher: Χρυσόστομος Δούκας
- Teacher: Ιωάννης Ψαρράς
ECTS : 4
Study Load : theory 3, lab 0
Language : el
Learning Outcomes : Upon successful completion of the course, students will be able to:
- Understand the basic principles, concepts and techniques of operations management and multi-criteria decision-making (problem formulations, trade-offs between criteria, decision environments, consistency of a family of criteria, criteria weights, performances of alternatives, aggregation of decision-criterion functions, etc.), as well as the potential for linking multi-criteria analysis with different scientific fields through a wide range of real-world applications.
- Possess and utilize specialized skills for solving complex multi-criteria decision-making problems, which are required in operations research, the operations management of a business or any organization, as well as in innovation.
- Manage complex and unpredictable work environments that require innovative strategic approaches, by selecting and employing modern and specialized multi-criteria analysis methodologies (including modern extensions of traditional decision support models), such as methods based on Euclidean distance from ideal points, outranking relations, analysis–synthesis approaches, and aggregation of heterogeneous variables.