Multiple Attribute Decision Analysis Research Articles

On the evidential reasoning rule for dependent evidence combination

Evidential Reasoning (ER) rule, which can combine multiple pieces of independent evidence conjunctively, is widely applied in multiple attribute decision analysis. However, the assumption of independence among evidence is often not satisfied, resulting in ER rule inapplicable. In this paper, an Evidential Reasoning rule for Dependent Evidence combination (ERr-DE) is developed. Firstly, the aggregation sequence of multiple pieces of evidence is determined according to evidence reliability. On this basis, a calculation method of evidence Relative Total Dependence Coefficient (RTDC) is proposed using the distance correlation method. Secondly, as a discounting factor, RTDC is introduced into the ER rule framework, and the ERr-DE model is formulated. The aggregation process of two pieces of dependent evidence by ERr-DE is investigated, which is then generalized to aggregate multiple pieces of non-independent evidence. Thirdly, sensitivity analysis is carried out to investigate the relationship between the model output and the RTDC. The properties of sensitivity coefficient are explored and mathematically proofed. The conjunctive probabilistic reasoning process of ERr-DE and the properties of sensitivity coefficient are verified by two numerical examples respectively. Finally, the practical application of the ERr-DE is validated by a case study on the performance assessment of satellite turntable system.

An extension of the FLOWSORT method based on intuitionistic fuzzy set theory to solve Multicriteria Decision Making problems

Multiple attribute decision analysis (MADA) is an important part of the modern decision science. In fact, it has been increasingly used in the literature. Therefore, the study of sorting problems is an active research topic in the multiple criteria decision aid (MCDA) area. Although, it is difficult to quantitatively and precisely express the evaluation criteria to solve real-life sorting problems. To solve this fuzziness and vagueness, the intuitionistic fuzzy set (IFS) theory achieved great success in various recent researches. Therefore, this paper presents a novel extension of the FlowSort method, which is a PROMETHEE-based sorting method, based on the intuitionistic fuzzy set theory. To clarify this new extension, an illustrative example and an empirical comparison with other MCDM methods are presented.

Comparison of multicriteria analysis techniques for decision making on floating offshore wind farms site selection

This work compares two methodologies to assess different selection approaches, the Analytic Hierarchy Process (AHP) and Multiple Attribute Decision Analysis (MADA) using a combined Evidential Reasoning (ER) and AHP approach. This evaluation is done depending on: the number of alternative criteria, agility through the process of decision-making, computational complexity, adequacy in supporting a group decision, and consistency of results. Case studies are presented to analyze the robustness of the methodology evaluation. The criteria used to evaluate and identify the best locations are adapted for each methodology to proceed with the comparison. The results show that each approach is suitable for the problems of wind farm location selection, particularly toward the support of group decision-making and uncertainty modelling. The sites are ranked based on their respective weights for AHP and MADA. In terms of computational complexity, the complete AHP method performs better than the combined MADA and AHP approaches. Nevertheless, the MADA method is less time-consuming and convenient for selecting floating farm locations due to the smaller involvement of experts and corresponding higher agility during decision-making. Both methodologies demonstrate several alternative processes and criteria, adequacy in supporting a group decision, and adaptation in terms of criteria insertion or removal.

Development and application of a multiple-attribute decision-analysis methodology for site selection of floating offshore wind farms on the UK Continental Shelf

This research presents the development of a methodology for determining the most suitable floating offshore wind farm locations for the northern coast of Scotland, through the application of multi-attribute decision-analysis. A large area off the northern coast of Scotland is defined and separated into coordinate grids. The environmental, logistical and facilities factors are first analysed in order to remove sites that fall within restricted areas. Following this, data is gathered for the remaining sites in terms of a set of Logistics, Facilities & Environmental, and Met-Ocean criteria. The logistical criterion consists of such factors as, depth, distance to ports and distance to substations. The Met-ocean criterion provides a data analysis of the wind, wave, tidal and current conditions of each site between 2011 and 2016, and the Facilities & Environmental criterion analyses the proximity of the sites to such criteria as Marine Protection Areas, Special Areas of Conservation, military training areas and subsea facilities. The compiled data is then applied to a Multiple Attribute Decision Analysis (MADA) algorithm which aggregates the data for each site and produces a utility ranking in order to determine the most suitable site for floating offshore wind. Validation is conducted through benchmark testing and correlation with government survey sites.

Analysis of the Optimal Application of Blockchain-Based Smart Lockers in the Logistics Industry Based on FFD-SAGA and Grey Decision-Making

Blockchain technology has been applied to logistics tracking, but it is not cost-effective. The development of smart lockers has solved the problem of repeated distribution to improve logistics efficiency, thereby becoming a solution with convenience and privacy compared to the in-store purchase and pickup alternative. This study prioritized the key factors of smart lockers using a simulated annealing–genetic algorithm by fractional factorial design (FFD-SAGA) and grey relational analysis, and investigated the main users of smart lockers by grey multiple attribute decision analysis. The results show that the Web application programming interface (API) concatenation and money flow provider are the key success factors of smart lockers, and office workers are the main users of the lockers. Hence, how to better meet the needs of office workers will be an issue of concern for service providers.

A Generic Feature Extraction Approach for Dealing with Multiple Attribute Decision Analysis Problems under Risk and Uncertainty

This paper represents a generic feature extraction approach to handle multiple attribute decision analysis problems. For that purpose, available decision support frameworks are carefully studied and the basic types of attributes involved in the decision problems are identified. Based on this analysis, a generic decision support scheme is proposed that can deal with all sorts of attributes in order to deduce the optimal solution for any decision problem. The proposed framework is capable of handling multiple attributes throughout the process of providing a flawless solution for the decision problem under both risk and uncertainty. This paper provides detailed information about the sources of uncertainty in the decision-making process and proposes a sophisticated approach for capturing all sorts of uncertainties. In the proposed approach, a cross assessment of every attribute against the corresponding attribute of the other alternatives is conducted to extract the significant features of an attribute. The relative importance of every attribute is considered as a supporting knowledge representation parameter in order to optimize the attribute-assessment process. The final decision is made based on the numerical scores seized by the alternatives. The paper also represents a numerical study to demonstrate the potential applications of the proposed methodology.

A Generic Feature Extraction Approach for Dealing with Multiple Attribute Decision Analysis Problems under Risk and Uncertainty

A Generic Feature Extraction Approach for Dealing with Multiple Attribute Decision Analysis Problems under Risk and Uncertainty

Regression model for estimation of attribute reliability

In this paper, a regression model is developed to estimate attribute reliability in the evidential reasoning (ER) context. By analysing the difference between attribute weight and attribute reliability, a general qualitative definition of attribute reliability is provided. The reliability of an attribute is quantitatively measured in consistence with the qualitative definition in the context of the ER approach. A regression model is then constructed to generate attribute reliabilities by minimising the maximum differences between the real value of attribute reliability and its estimation. Within the post-optimal solution space of attribute reliabilities, an optimisation model is constructed to determine the expected utilities of each alternative in order to generate solutions to multiple attribute decision analysis problems. A sale place selection problem in Qingyang County of Chizhou in Anhui province of China is analysed using the proposed regression model to demonstrate its detailed implementation process, validity and applicability.

A New MADA Methodology Based on D Numbers

Multiple attribute decision analysis (MADA) is still a hot issue. In order to represent information of attribute more flexibly and intuitively, quantitative and qualitative data are involved in real-world applications. In this paper, a new methodology to deal with MADA problem based on D numbers is proposed. Different with other existing methods, the new proposed method is more simply and straightforward, due to the integration property of D numbers. A numerical example of car assessment is used to demonstrate the effectiveness of proposed method.

Determining attribute weights for multiple attribute decision analysis with discriminating power in belief distributions

Attribute weight assignment plays an important role in multiple attribute decision analysis (MADA). When the performances of alternatives on each attribute are expressed by distributions instead of single values, how to use the differences among the performances to obtain attribute weights is an interesting but difficult issue. To address this issue, in this paper, we propose a method for obtaining attribute weights from discriminating power in belief distributions. With the consideration of the differences among the utilities of all assessment grades used to profile belief distributions, the dissimilarity based discriminating power, the standard deviation based discriminating power and the Gini's mean difference based discriminating power of the performances of all alternatives on each attribute are constructed to determine three sets of respective weights of attributes. They are convexly combined using three coefficients to generate integrated weights of attributes. To relieve the burden on a decision maker to provide precise values for the three coefficients, they are allowed to change between 0 and 1, as long as their sum is equal to 1. Under such constraints on the three coefficients, an optimization model is constructed to determine the minimum and maximum expected utilities of each alternative. From the expected utilities, all alternatives are then compared using Hurwicz rule with the provided optimism degree interval to generate solutions to MADA problems. The transitivity of the comparison outcomes among three alternatives under a given optimism degree interval is theoretically analyzed to guarantee the rationality of the outcomes. A focal form selection problem is investigated to demonstrate the applicability and validity of the proposed method.

Hybrid method for assessing marketable fashion designs: diamond pairwise comparison combined with the Choquet integral

Various multiple-attribute decision analysis approaches have been developed to choose the best fashion design among alternatives, but none of them consider interdependence among their criteria. This study combined the diamond pairwise comparison method to elicit the weights and interaction degrees of a set of criteria and the Choquet integral method for aggregating the evaluation to identify the best design based on the decision-makers’ preferences and prospects. The criteria were determined from input by 10 experts in diverse areas related to fashion design. By implementing the hybrid method in a case example, the efficacy of the proposed approach for evaluating fashion designs is demonstrated.

Coordinated scheduling strategy to optimize conflicting benefits for daily operation of integrated electricity and gas networks

The increasing share of variable renewable energy sources and the improving requirements on system security and reliability are calling for important changes in our energy systems. The synergies between energy supply networks are of great importance to satisfy the development of the integrated energy system (IES). Hence this paper presents the study of the coordinated scheduling strategy (CSS), in which, the models of the electricity network and gas network are developed in detail, and the operation constraints of the networks are fully considered. The purpose of the CSS is to optimize the conflicting benefits of the electricity network and gas network for daily operation of the IES, while satisfying the operation constraints. In the CSS, a multi-objective optimization algorithm is applied to obtain a Pareto-optimal solution set, and a multiple attribute decision analysis (MADA) using interval evidential reasoning (IER) is developed to determine a final optimal daily operation solution for the IES. Simulation studies are conducted on an IES consisting of a modified IEEE 30-bus electricity network and a 15-node gas network to verify the effectiveness of the CSS, and to evaluate the interdependency between the electricity network and gas network.

Distributed preference relations for multiple attribute decision analysis

In this paper, we propose a new pairwise comparison approach called distributed preference relation (DPR) to simultaneously signify preferred, non-preferred, indifferent, and uncertain degrees of one alternative over another on a set of grades, which is more versatile for elicitation of preference information from a decision maker than multiplicative preference relation, fuzzy preference relation (FPR) and intuitionistic FPR. In a DPR matrix on a set of alternatives, each element is a distribution recording the preferred, non-preferred, indifferent, and uncertain degrees of one alternative over another using a set of grades. To facilitate the comparison of alternatives, we define a score matrix based on a DPR matrix using the given score values of the grades. Its additive consistency is constructed, analysed, and compared with the additive consistency of FPRs between alternatives. A method for comparing two interval numbers is then employed to create a possibility matrix from the score matrix, which can generate a ranking order of alternatives with possibility degrees. A problem of evaluating strategic emerging industries is investigated using the approach to demonstrate the application of a DPR matrix to modelling and analysing a multiple attribute decision analysis problem.

An Extended VIKOR Method for Multiple Attribute Decision Analysis with Bidimensional Dual Hesitant Fuzzy Information

Bidimensional dual hesitant fuzzy (BDHF) set is developed to present preferences of a decision maker or an expert, which is more objective than existing fuzzy sets such as Atanassov’s intuitionistic fuzzy set, hesitant fuzzy set, and dual hesitant fuzzy set. Then, after investigating some distance measures, we define a new generalized distance measure between two BDHF elements with parameterλfor the sake of overcoming some drawbacks in existing distance measures. Covering all possible values of parameterλ, a new approach is designed to calculate the generalized distance measure between two BDHF elements. In order to address complex multiple attribute decision analysis (MADA) problems, an extension of fuzzy VIKOR method in BDHF context is proposed in this paper. In VIKOR method for MADA problems, weight of each attribute indicates its relative importance. To obtain weights of attributes objectively, a new entropy measure with BDHF information is developed to create weight of each attribute. Finally, an evaluation problem of performance of people’s livelihood project in several regions is analyzed by the proposed VIKOR method to demonstrate its applicability and validity.

Appraisal of global CO2 storage opportunities using the geomechanical facies approach

Different tectonic settings exert different depositional process controls within the tectonic basin which influence CO2 storage site suitability in terms of basin architecture, caprock architecture, reservoir quality, stress state, mechanical characteristics, fractures, burial depth, geothermal gradient, risk of orogenic modification, structural stability and preservation potential. We apply the concept of geomechanical facies; where deposits are grouped together on the basis of engineering parameters that fulfil a specific role within the storage system, e.g. reservoir, caprock, overburden; to provide an assessment of CO2 storage suitability for seven different tectonic settings. The geomechanical facies data were analysed using two different multiple attribute decision analysis methodologies and the results show that foreland basins are likely to be the most suitable for CO2 storage, followed by passive continental margins, terrestrial rift basins, Strike-slip basins, Back-arc basins, with oceanic basins, forearc basins and trench basins expected to be the least suitable. The geomechanical facies approach was compared with two current storage projects (Sleipner and In Salah) and a natural CO2 storage analogue (Miller) to build confidence in the methodology. Finally the global distribution of the most likely CO2 storage basins based on their geomechanical facies was mapped and correlated with CO2 emission sources.

Induced Ordered Weighted Evidential Reasoning Approach for Multiple Attribute Decision Analysis with Uncertainty

We are primarily concerned with the problem of aggregating multiple attributes with uncertainty to form an overall decision function. We introduce a new type of approach for aggregation called an induced ordered weighted evidential reasoning IOWER approach, which is inspired by an induced ordered weighted averaging operator and the evidential reasoning ER approach. In the IOWER approach, we use a belief decision matrix combined with an induced ordered weighting vector for problem modeling and the Dempster-Shafer theory of evidence for attribute aggregation. It is proved that the original ER algorithm is a special case of the IOWER algorithm. Then we examine the properties of the IOWER approach. One key point in the IOWER approach is to reorder the arguments in the form of distributed assessment structure. A kind expected utility order-inducing variable is proposed in the IOWER approach, which can make the alternative's advantages prominent. Finally, we present an illustrative example in which the result obtained with the new aggregation approach can be seen.

Determining attribute weights to improve solution reliability and its application to selecting leading industries

In multiple attribute decision analysis, many methods have been proposed to determine attribute weights. However, solution reliability is rarely considered in those methods. This paper develops an objective method in the context of the evidential reasoning approach to determine attribute weights which achieve high solution reliability. Firstly, the minimal satisfaction indicator of each alternative on each attribute is constructed using the performance data of each alternative. Secondly, the concept of superior intensity of an alternative is introduced and constructed using the minimal satisfaction of each alternative. Thirdly, the concept of solution reliability on each attribute is defined as the ordered weighted averaging (OWA) of the superior intensity of each alternative. Fourthly, to calculate the solution reliability on each attribute, the methods for determining the weights of the OWA operator are developed based on the minimax disparity method. Then, each attribute weight is calculated by letting it be proportional to the solution reliability on that attribute. A problem of selecting leading industries is investigated to demonstrate the applicability and validity of the proposed method. Finally, the proposed method is compared with other four methods using the problem, which demonstrates the high solution reliability of the proposed method.

Potential optimality and robust optimality in multiattribute decision analysis with incomplete information: A comparative study

The traditional approach for multiple attribute decision analysis with incomplete information on alternative values and attribute weights is to identify alternatives that are potentially optimal. However, the results of potential optimality analysis may be misleading as an alternative is evaluated under the best-case scenario of attribute weights only. Robust optimality analysis is a conservative approach that is concerned with an assured level of payoff for an alternative across all possible scenarios of weights. In this study, we introduce two measures of robust optimality that extend the robust optimality analysis approach and classify alternatives in consideration into three groups: strong robust optimal, weak robust optimal and robust non-optimal. Mathematical models are developed to compute these measures. It is claimed that robust optimality analysis and potential optimality analysis together provide a comprehensive picture of an alternative's variable payoff.

Group Evidential Reasoning Approach for MADA under Fuzziness and Uncertainties

Abstract Multiple attribute decision analysis (MADA) problems often include both qualitative and quantitative attributes which may be either precise or inaccurate. The evidential reasoning (ER) approach is one of reliable and rational methods for dealing with MADA problems and can generate aggregated assessments from a variety of attributes. In many real world decision situations, accurate assessments are difficult to provide such as in group decision situations. Extensive research in dealing with imprecise or uncertain belief structures has been conducted on the basis of the ER approach, such as interval belief degrees, interval weights and interval uncertainty. In this paper, the weights of attributes and utilities of evaluation grades are considered to be fuzzy numbers for the ER approach. Fuzzy analytic hierarchy process (FAHP) is used for generating triangular fuzzy weights for attributes from a triangular fuzzy judgment matrix provided by an expert. The weighted arithmetic mean method is proposed to...

An evidential reasoning based classification algorithm and its application for face recognition with class noise

For classification problems, in practice, real-world data may suffer from two types of noise, attribute noise and class noise. It is the key for improving recognition performance to remove as much of their adverse effects as possible. In this paper, a formalism algorithm is proposed for classification problems with class noise, which is more challenging than those with attribute noise. The proposed formalism algorithm is based on evidential reasoning theory which is a powerful tool to deal with uncertain information in multiple attribute decision analysis and many other areas. Thus, it may be more effective alternative to handle noisy label information. And then a specific algorithm—Evidential Reasoning based Classification algorithm (ERC) is derived to recognize human faces under class noise conditions. The proposed ERC algorithm is extensively evaluated on five publicly available face databases with class noise and yields good performance.