Improving discrimination in efficiency analysis of bioethanol processes

Gavião, Luiz Octávio; Meza, Lídia Ângulo; Lima, Gílson Brito Alves; Sant’Anna, Annibal Parracho; Mello, João Carlos Correia Baptista Soares de

doi:10.1016/j.jclepro.2017.06.020

Cited by 12 publications

(4 citation statements)

References 49 publications

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“…The two subsequent stages of CPP remain unchanged and are described in detail in several published articles and books, with a wide range of applications, including sports science [35], management systems [43,44], security and defense [45], public health and social assistance [46,47], risk management [48], and bioenergy processes [36].…”

Section: Methodsmentioning

confidence: 99%

“…In the last stage, the probabilities of each alternative being the best or worst in each criterion are combined in different ways, providing different points of view for decision-making. For instance, PMax and PMin can be composed by axis (progressiveconservative/optimist-pessimist) [32], by principles of concentration/dilution [33], by Choquet capacities [34], by the Gini index [35], by classes [2], and associated with other methods [36]. Automation of the complete CPP procedure is available with open access in the "CPP" package [37].…”

Section: The Basics Of Cppmentioning

confidence: 99%

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Composition of Probabilistic Preferences in Multicriteria Problems with Variables Measured in Likert Scales and Fitted by Empirical Distributions

Gavião¹,

Sant’Anna

Lima

et al. 2023

Standards

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The aim of this article is to demonstrate the advantages of the Composition of Probabilistic Preferences method in multicriteria problems with data from Likert scales. Multicriteria decision aids require a database as a decision matrix, in which two or more alternatives are evaluated according to two or more variables selected as decision criteria. Several problems of this nature use measures by Likert scales. Depending on the method, parameters from these data (e.g., means, modes or medians) are required for calculations. This parameterization of data in ordinal scales has fueled controversy for decades between authors who favor mathematical/statistical rigor and argue against the procedure, stating that ordinal scales should not be parameterized, and scientists from other areas who have shown gains from the process that compensates for this relaxation. The Composition of Probabilistic Preferences can allay the protests raised and obtain more accurate results than descriptive statistics or parametric models can bring. The proposed algorithm in R-code involves the use of probabilities with empirical distributions and fitting histograms of data measured by Likert scales. Two case studies with simulated datasets having peculiar characteristics and a real case illustrate the advantages of the Composition of Probabilistic Preferences.

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Section: Methodsmentioning

confidence: 99%

Section: The Basics Of Cppmentioning

confidence: 99%

Composition of Probabilistic Preferences in Multicriteria Problems with Variables Measured in Likert Scales and Fitted by Empirical Distributions

Gavião¹,

Sant’Anna

Lima

et al. 2023

Standards

Self Cite

View full text Add to dashboard Cite

show abstract

Section: The Basics Of Cppmentioning

confidence: 99%

Composition of Probabilistic Preferences in Multicriteria Problems With Variables Measured in Likert Scales and Fitted by Empirical Distributions

Gavião¹,

Sant’Anna²,

Lima³

et al. 2023

Preprint

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Multicriteria decision aid requires a database as a decision matrix, in which two or more alternatives are evaluated according to two or more variables selected as decision criteria. Several problems of this nature use measures by Likert scales. Depending on the method, parameters from these data (e.g. means, modes or medians) are required for calculations. This parameterization of data in ordinal scales has fueled controversy for decades between authors who favor mathematical/statistical rigor and argue against the procedure, stating that ordinal scales should not be parameterized, and scientists from other areas who have shown gains from the process that compensate for this relaxation. The aim of this article is to demonstrate the advantages of the Composition of Probabilistic Preferences (CPP) method in multicriteria problems with data from Likert scales. The CPP is capable of allaying the protests raised and obtaining more accurate results than descriptive statistics or parametric models can bring. The proposed algorithm in R-code involves the use of CPP with empirical distributions and fitting histograms of data measured by Likert scales. Two case studies with simulated datasets having peculiar characteristics and a real case illustrate the advantages of the CPP.

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“…These data can also be found in catalogs, used as technical references by the defense industry. The tabulation of the systems of interest and the performances in their respective characteristics composes the database for the application of a multicriteria decision support model (Almeida, 2013;Gavião et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Decision support based on performance data using the analytic hierarchy process without expert judgement

Gavião,

Lima,

Garcia

et al. 2024

BJO&PM

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Goal: This article proposes a decision model based on the Analytic Hierarchy Process that allows carrying out the evaluation of alternatives in a multicriteria problem, without expert judgement. Design / Methodology / Approach: The algorithm is based on AHP. The novelty is the transformation of a performance data matrix into pairwise evaluation matrices, instead of using experts’ judgement. Results: The algorithm was applied in a defense procurement problem for the choice of a light 4x4 vehicle for amphibious operations. The results allowed ranking the 17 models based on catalog data. Limitations of the investigation: the algorithm depends on the availability of catalog data, not always available in open sources in the defense industry. Practical implications: Decision support involves several activities in Operations Management and AHP has been frequently applied to solve problems in this sector. The proposed algorithm allows performing deterministic or probabilistic evaluations, depending on the degree of uncertainty and precision involving the systems’ performance data. These assessments are composed of scenarios to facilitate decision making. Originality / Value: AHP typically uses experts for pairwise judgments. However, human judgment is subject to outcomes that involve bias and cognitive distortions. Few studies have modeled the AHP without experts, even so they used human judgment in some part of the process. The approach proposed here does not require human judgment and returns two different results, based on the database precision. This new approach gives decision makers a different perspective and can alter the final choice.

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Improving discrimination in efficiency analysis of bioethanol processes

Cited by 12 publications

References 49 publications

Composition of Probabilistic Preferences in Multicriteria Problems with Variables Measured in Likert Scales and Fitted by Empirical Distributions

Composition of Probabilistic Preferences in Multicriteria Problems with Variables Measured in Likert Scales and Fitted by Empirical Distributions

Composition of Probabilistic Preferences in Multicriteria Problems With Variables Measured in Likert Scales and Fitted by Empirical Distributions

Decision support based on performance data using the analytic hierarchy process without expert judgement

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