Evaluating future scenarios for the power generation sector using a Multi-Criteria Decision Analysis (MCDA) tool: The Portuguese case

Ribeiro, Fernando José da Silva Paiva; Ferreira, Paula Varandas; Araújo, Maria Madalena Teixeira de

doi:10.1016/j.energy.2012.12.036

Cited by 132 publications

(54 citation statements)

References 28 publications

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“…This assessment is similar to a simple additive approach, in which the scenario with the highest score is the best option. Ribeiro et al [17] designed and used an MCDM tool to support the sustainability assessment of different electricity production scenarios for Portugal in 2020. The proposed method can be summarized as direct weighting with an additive approach, where the best solution is the one with the highest score.…”

Section: Mcdm (Multi-criteria Decision Making) Methods Applied In Thementioning

confidence: 99%

Assessing the global sustainability of different electricity generation systems

Barros

Coira

Cruz

et al. 2015

Energy

106

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Section: Mcdm (Multi-criteria Decision Making) Methods Applied In Thementioning

confidence: 99%

Assessing the global sustainability of different electricity generation systems

Barros

Coira

Cruz

et al. 2015

Energy

106

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“…This is the reason why a wide range of MCDA methods is used in this area which apply different methodologies in order to model the decision situation and the involved uncertainties (cf. Browne et al, 2010;Heo et al, 2010;Kaya and Kahraman, 2011;Streimikiene et al, 2012;Ribeiro et al, 2013;Ren et al, 2013;Lühn et al, 2014). For literature reviews of the application of MCDA methodologies in energy decision situations please refer to Greening and Bernow, 2004;Pohekar and Ramachandran, 2004;Diakoulaki et al, 2005;Zhou et al, 2006;Loken, 2007;Kowalski et al, 2009;Wang et al, 2009;Abu-Taha, 2011 andScott et al, 2012. This paper is structured as follows: In section 2, we review and summarise relevant literature related to (i) preferential uncertainty modelling in MCDA, (ii) benefits of explanation systems in general and (iii) existing explanatory features in MCDA tools.…”

Section: Introductionmentioning

confidence: 99%

A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making

Wulf

Bertsch

2017

Expert Systems with Applications

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A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making Abstract Multi-Criteria Decision Analysis (MCDA) enables decision makers (DM) and decision analysts (DA) to analyse and understand decision situations in a structured and formalised way. With the increasing complexity of decision support systems (DSSs), it becomes challenging for both expert and novice users to understand and interpret the model results. Natural language generation (NLG) techniques are used in various DSSs to cope with this challenge as they reduce the cognitive effort to achieve understanding of decision situations. However, NLG techniques in MCDA have so far mainly been developed for deterministic decision situations or one-dimensional sensitivity analyses. In this paper, a concept for the generation of textual explanations for a multi-dimensional preferential sensitivity analysis in MCDA is developed. The key contribution is a NLG approach that provides detailed explanations of the implications of preferential uncertainties in Multi-Attribute Value Theory (MAVT). It generates a report that assesses the influences of simultaneous or separate variations of inter-criteria and intra-criteria preferential parameters determined within the decision analysis. We explore the added value of the natural language report in an online survey. Our results show that the NLG approach is particularly beneficial for difficult interpretational tasks. IntroductionWith the aim of enabling transparent and systematic support in complex decision situations, Multi-Criteria Decision Analysis (MCDA) represents a formalised framework for the analysis of different decision alternatives (Stewart, 1992;Geldermann et al., 2009). While such decision support approaches are aimed at providing guidance to decision makers (DMs), their increasing mathematical complexity often hinders a straightforward understanding and traceability on the part of the DMs. Consequently, a lot of cognitive effort is required in order to analyse, interpret and derive adequate implications from the obtained model results which is particularly challenging for novice users (Spiegelhalter and KnillJones, 1984;Henrion and Druzdzel, 1991;Gregor and Benbasat, 1999). DMs consider such models as a 'black box', so they mistrust or even reject them (Brans and Mareschal, 1994;Bell et al., 2003), which leads to a gap between available information on the one hand and processible information on the other hand.To compensate for this, further explanations of decision analysis results promote understanding of the decision situation and thus help to increase trust and acceptability of the 3 system (Greer et al., 1994;Greef and Neerincx, 1995;Dhaliwal and Benbasat, 1996;Gregor and Benbasat, 1999;Parikh et al., 2001;Geldermann, 2010). The use of natural language generation (NLG) techniques to generate such explanations automatically based on the model results has been proposed, for instance, by Pap...

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“…Different scenarios were evaluated based on AHP method in that study. Ribeiro et al (2013) evaluated future scenarios based on MCDA model. They just analyzed the scenarios based on a set of criteria.…”

Section: Gonzalez-prida Et Al (2014) Presented a New Dynamic Model Fmentioning

confidence: 99%

Madm and Futures Studies; A Necessity

Zolfani

Maknoon

Zavadskas

2016

9th International Scientific Conference “Business and Management 2016”

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Abstract. Multiple Attribute Decision Making (MADM) has been developing in different methods, perspectives and frameworks since introducing step. Futures Studies as a specialized framework and methodology has introduced newer and has been always in developing phase too. MADM as a part of Multiple Criteria Decision Making is known as multi-disciplinary approach, framework and methodology. Nowadays, Futures Studies is also known as multi-disciplinary approach too. Basically, MADM is structured for a stable environment while most decisions need to be made, dynamically. Time is so much important especially in the new century in comparison with the past. Decisions making about future are usually so complicated and MADM can be helpful in that process. Importance of making future based decisions is undeniable in trying to answer to decision needs. This research will present a comprehensive review on the literature of MADM and new orientations in considering future in MADM models and necessity of them will be checked also carefully. Eventually, importance of seeing both MADM and Futures Studies together as a unit model will be discussed in this study.

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Evaluating future scenarios for the power generation sector using a Multi-Criteria Decision Analysis (MCDA) tool: The Portuguese case

Cited by 132 publications

References 28 publications

Assessing the global sustainability of different electricity generation systems

Assessing the global sustainability of different electricity generation systems

A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making

Madm and Futures Studies; A Necessity

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