Fuzzy Multi-actor Multi-criteria Decision Making for sustainability assessment of biomass-based technologies for hydrogen production

Ren, Jingzheng; Fedele, Andrea; Mason, Marco; Manzardo, Alessandro; Scipioni, Antonio

doi:10.1016/j.ijhydene.2013.05.074

Cited by 106 publications

(36 citation statements)

References 37 publications

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“…Many instances of the applications of fuzzy MCDM methods can be found in [47,48], where it was highlighted that most of the applications had selected to implement variants based on AHP. In [49], a novel fuzzy multi-actor MCDM method was used in an application of hydrogen technology, where 15 criteria were used for the sustainability assessment. In [17], during deterministic TOPSIS, the weights for each criterion were considered fixed, but under stochastic modelling, statistical distributions were employed to best fit the acquired data of the experts' opinions.…”

Section: Review Of the Stochastic Expansion Of Deterministic Mcdmmentioning

confidence: 99%

A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs

et al. 2016

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This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative.

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Section: Review Of the Stochastic Expansion Of Deterministic Mcdmmentioning

confidence: 99%

A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs

et al. 2016

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“…Group decision making is becoming an increasingly important part of multiple criteria decision making (Ahmad, Saman, Mohamad, Mohamad, & Awang, 2014;De Brucker, Macharis, & Verbeke, 2013;Ishizaka & Labib, 2011a;Kuzman, Grošelj, Ayrilmis, & Zbasnik-Senegacnik, 2013;Ren, Fedele, Mason, Manzardo, & Scipioni, 2013;Skorupski, 2014;Wang, Peng, Zhang, & Chen, 2014;Yu & Lai, 2011). Multiple stakeholders can contribute a variety of experiences, expertise and perspectives, and a group can better deal with the complexity of the problem than a single decision maker (DM).…”

Section: Introductionmentioning

confidence: 99%

Comparison of some aggregation techniques using group analytic hierarchy process

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et al. 2015

Expert Systems with Applications

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a b s t r a c tGroup decision making is an important part of multiple criteria decision making and the analytic hierarchy process (AHP). The aim of this paper was to compare group AHP methods. Seven simple group AHP aggregation techniques that could be attractive for applications selected from the vast array of group AHP models proposed in the literature were selected for evaluation. We developed three new measures of evaluation: group Euclidean distance, group minimum violations, and distance between weights for the purpose of evaluation. The results of seven group AHP methods of the theoretical example were evaluated by three new evaluation measures, satisfactory index and fitting performance index. Furthermore, a case study of a decision making problem from the construction engineering field was performed and nine group AHP aggregation techniques, seven of them formerly presented and two new two stage group approaches were applied. Finally, the case study was evaluated using all five measures for each of the nine group decision making methods. The results showed that not all group AHP methods are equally convenient and that the selection of the method depended on the specific application.

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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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Fuzzy Multi-actor Multi-criteria Decision Making for sustainability assessment of biomass-based technologies for hydrogen production

Cited by 106 publications

References 37 publications

A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs

A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs

Comparison of some aggregation techniques using group analytic hierarchy process

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

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