IMP: A decision aid for multiattribute evaluation using imprecise weight estimates

Jessop, Alan

doi:10.1016/j.omega.2014.05.001

Cited by 24 publications

(12 citation statements)

References 97 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The application of precise preference information in the DSS might therefore not be feasible motivating the application of parameter ranges for one or multiple preference parameters (Ríos Insua and French, 1991;Butler et al, 1997;Matsatsinis and Samaras, 2001;Jiménez et al, 2005;Mustajoki et al, 2005;Mateos et al, 2006;Mavrotas and Trifillis, 2006;Jessop, 2011;Jessop, 2014;Scholten et al, 2015).…”

Section: Modelling Of Multiple Preferential Uncertaintiesmentioning

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

View full text Add to dashboard Cite

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...

show abstract

Section: Modelling Of Multiple Preferential Uncertaintiesmentioning

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

View full text Add to dashboard Cite

show abstract

“…The relative preferences are then aggregated to acquire the outranking relations which represent the dominance degree of one alternative over others. The widely-used outranking methods are ELECTRE (ELimination Et Choix Traduisant la REalité in French, ELimination and Choice Expressing the Reality) [24], PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) [25], [26], GLDS (Gained and Lost Dominance Score) method [27], [28], AHP (Analytical Hierarchy Process) [29], [30] and ANP (Analytical Network Process) [31], Superiority and Inferiority Ranking (SIR) method [32], Step-Wise Weight Assessment Ratio Analysis (SWARA) [33], subjective weighting method using continuous interval scale [34], multi-attribute evaluation using Imprecise Weight Estimates (IMP) [35] and, more recently, the Best-Worst Method [13]- [15], [36]- [40]. Such approaches are effective in circumstances where it is difficult or meaningless to include the measured scores for candidate alternatives w.r.t.…”

Section: ) Anything-as-a-service (Xaas)mentioning

confidence: 99%

“…ξ * is computed from equation (24) and ξ max varies for different values of p BW as shown in Table 16. In AHP, Consistency Index (CI) and Consistency Ratio (CR) are computed using equations (35) and (38), where λ max denotes the largest eigenvalue of pairwise matrix (nxn) and n represents no. of criteria or alternatives.…”

Section: B Consistency (Reliability)mentioning

confidence: 99%

An Integrated MCDM Approach for Cloud Service Selection Based on TOPSIS and BWM

Youssef

2020

IEEE Access

View full text Add to dashboard Cite

Cloud Computing (CC) has become increasingly popular since it provides a wide variety of customized and reliable computational services. With the rapid growth of this technology, more and more IT services providers compete to offer high-quality and cost-effective cloud services that best fulfill their customers' needs. Given the vast diversity of these offers, the choice of the most appropriate Cloud Service Provider (CSP) became a dilemma that confuses most cloud customers. Many diverged criteria have to be considered to precisely evaluate services offered by several CSPs, some of these criteria cannot be quantified easily such as usability and security. The selection of the best CSP is thus a complex Multi-Criteria Decision Making (MCDM) problem that needs to be addressed efficiently. Previous studies of this problem employed MCDM methods that are either unfeasible when it is difficult or meaningless to quantify alternatives over criteria or computationally expensive and inconsistent when relative preferences of alternatives and criteria are used instead. In this paper, we propose a novel MCDM approach that is feasible, efficient and consistent using relative preferences of criteria and alternatives. The proposed approach incorporates Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Best Worst Method (BWM) to rank CSPs using evaluation criteria characterizing their services. The integrated approach has been tested and validated through a use-case scenario which demonstrates its effectiveness and correctness. We have also compared the proposed approach to the most commonly used MCDM approach, Analytical Hierarchical Process (AHP). The results clearly show that the proposed approach outperforms AHP in terms of computational complexity and consistency; hence, it is more efficient and reliable than AHP. INDEX TERMS Cloud computing (CC), cloud service providers (CSPs), multiple-criteria decision-making (MCDM), best worst method (BWM), technique for order of preference by similarity to ideal solution (TOPSIS), analytical hierarchical process (AHP).

show abstract

“…MCDM is one of the most widely used methodologies in fields like business and economy (Mardani et al, 2015; Rabbani, Zamani, Yazdani‐Chamzini, and Zavadskas, 2014). Commonly used MCDM techniques include Analytic Hierarchy Process (AHP); Analytic Network Process (ANP); VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR); Grey Relational Analysis (GRA); Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS); Non‐compensatory Multi‐criteria (NCMCs), Decision Aid for Multi Attribute Evaluation Using Imprecise Weight Estimates by Jessop (2014), Best‐Worst Method (BWM) by Rezaei (2016) and Decision‐Making Trial and Evaluation Laboratory (DEMATEL) by The Science and Human Affairs Program of Battelle Memorial Institute of Geneva.…”

Section: Review Of the Literaturementioning

confidence: 99%