Archives: Lessons

Lecture 9: Develop your own decision support model

Dr Marco Cinelli, Singapore-ETH Centre (ETH Zurich), presented a stepwise approach to build decision support systems and introduced some software that can support it.

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Lecture 8: Green chemistry-based evaluation of nanoparticles synthesis

Dr Marco Cinelli, Singapore-ETH Centre (ETH Zurich), described how he fully embedded the MCDA process in his Ph.D. research.

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Lecture 7: Decision rules

Dr Marco Cinelli, Singapore-ETH Centre (ETH Zurich), explained an MCDA method for classification and ranking based on decision rules, called Dominance-based Rough Sets Approach (DRSA).

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Lecture 6: Outranking methods = ELECTRE and PROMETHEE – 2

Dr Marco Cinelli, Singapore-ETH Centre (ETH Zurich), illustrated two of the most common MCDA methods based on pairwise comparisons, ELECTRE and PROMETHEE.

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Lecture 6: Outranking methods = ELECTRE and PROMETHEE

Dr Marco Cinelli, Singapore-ETH Centre (ETH Zurich), illustrated two of the most common MCDA methods based on pairwise comparisons, ELECTRE and PROMETHEE.

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Lecture 5: Case study 1: Electricity resilience

Dr Marco Cinelli, Singapore-ETH Centre (ETH Zurich), described the approach used for the development of resilience indices for countries from an energy supply perspective.

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Lecture 4: Multi Attribute Value Theory (MAVT)

Dr Marco Cinelli, Singapore-ETH Centre (ETH Zurich), presented the Multi Attribute Value Theory (MAVT) method, with a focus on the elicitation of weights and creation of value functions.

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Lecture 3: “Simple” averages

Dr Marco Cinelli, Singapore-ETH Centre (ETH Zurich), provided an overview of the approaches that can be used to develop indices.

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Lecture 2: Problem structuring

Dr Marco Cinelli, Singapore-ETH Centre (ETH Zurich), focused on the problem structuring phase of the MCDA process, covering the actors involved, the types of most recurrent decision-making problems and the role of the assessment criteria.

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Lecture 1: Introduction

Dr Marco Cinelli, Singapore-ETH Centre (ETH Zurich), introduced the Multiple Criteria Decision Aiding (MCDA) process and the reasons for its widespread use and success.  

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