Multiple Attribute Decision Making Approaches Research Articles

A hybrid intelligent model based on logistic regression and fuzzy multiple-attribute decision-making for credit evaluation

. One of the crucial issues in data mining is to select an appropriate classification algorithm. Due to it usually involves many criteria, the duty of algorithm selection can be widely described as multiple-attribute decision-making (MADM) problems, including credit risk evaluation. Many different MADM approaches select classifiers based on different perspectives, and hence they might generate diverse classifiers' rankings. This paper aims to propose a hybrid intelligent model to overcome credit risk assessment problems based on logistic regression and the fuzzy MADM method. Firstly, the Ordinal Priority Approach (OPA) method evaluates attributes involved in credit risk problems by considering professional assessments of a decision-maker and calculates a weight for each criterion. Secondly, all categorical data converted into triangular-fuzzy numbers (TFNs) and numerical data are evaluated using the MADM instrument to obtain an optimal solution dataset and logistic regression to calculate the probabilities of the optimal dataset. In this experimental study, three existing classification techniques and the proposed intelligent model evaluate three banking credit datasets with a different number of criteria under numerical and categorical data types. The prediction accuracy results generated by the proposed model are compared with the three existing classification methods. The results exhibit that there are slight differences between the three datasets. The experimental results demonstrate the proposed intelligent model has superiority in classifying the credit loan recipients especially for categorical datasets.

Multiple attribute decision making based on MAIRCA, standard deviation-based method, and Pythagorean fuzzy sets

In this paper, we propose a new multiple attribute decision making (MADM) approach based on the multi-attribute ideal-real comparative assessment (MAIRCA) method, the standard deviation-based method, and Pythagorean fuzzy sets. We also propose a weight-determining method to determine the weights of attributes using a standard deviation-based approach with Pythagorean fuzzy information. The drawbacks of the existing MADM methods in Pythagorean fuzzy environments are that they cannot distinguish the preference orders of alternatives in some situations. The proposed MADM approach can overcome the drawbacks of the existing MADM approaches in the Pythagorean fuzzy context. The main contributions of this paper include: (1) It proposes a weight-determining method to derive attributes’ weights based on the standard deviation-based method with Pythagorean fuzzy information, (2) It proposes a new MADM methodology based on the MAIRCA method, the standard deviation-based method, and PFSs for MADM in the Pythagorean fuzzy setting, and (3) The proposed MADM method can overcome the shortcomings of the existing MADM methods in the Pythagorean fuzzy context. It provides us a flexible and effective method for MADM in Pythagorean fuzzy environments.

Ranking range model in multiple attribute decision making: A comparison of selected methods

Multiple attribute decision making (MADM) approaches have been investigated extensively for ranking the alternatives related to multiple attributes. In most existing MADM approaches, attribute weights play a key role due to the fact that the ranking of alternatives may changes with attribute weights vector. Ranking range can be used to measure the lower and upper bounds of all possible rankings of alternatives when attribute weights are varying. In the paper, we investigate the ranking range for the selected seven popular MADM approaches: Weighted averaging (WA), weighted geometric averaging (WGA), ordered weighted averaging (OWA), ordered weighted geometric averaging (OWGA), TOPSIS, PROMETHEE and ELECTRE. Through determining the ranking ranges of alternatives, associated with attribute weights, in the selected seven approaches, we present several desirable properties of the ranking range. Then, we design the simulation experiments with either random or real data to compare the ranking range for selected seven MADM approaches. Interestingly, the experiment results clearly show that TOPSIS≻ELECTRE≻PROMETHEE~WA≻WGA≻OWA≻OWGA in the average sense, where '≻' denotes the larger ranking range, and '~' denotes no difference in the ranking range. A larger ranking range means an easier to manipulate the ranking of alternatives, and also means a worse robustness of a MADM approach.

Multiple attribute decision making based on q-rung orthopair fuzzy generalized Maclaurin symmetic mean operators

In the article, we establish two multiple attribute decision making (MADM) approaches using the developed weighted generalized Maclaurin symmetric mean (q-ROFWGMSM) and weighted generalized geometric Maclaurin symmetric mean (q-ROFWGGMSM) operator concerning q-rung orthopair fuzzy numbers (q-ROFNs). Firstly, inspired by the generalized Maclaurin symmetric mean (G-MSM) and geometric Maclaurin symmetric mean (Geo-MSM) operators, we establish the q-rung orthopair fuzzy G-MSM (q-ROFGMSM) and q-rung orthopair fuzzy Geo-MSM (q-ROFGGMSM) operators, which assumes the grades of membership and non-membership to evaluate information can take any values in interval [0,1] respectively and the attributes are relevant to other multiple attributes. Then, we present its characteristics and some special cases. Moreover, we propose the weighted forms of the q-ROFGMSM and q-ROFGGMSM operator, which is called the q-ROFWGMSM and q-ROFWGGMSM operators, respectively. Then, we present their some characteristics and special examples. Finally, we put forward two new MADM approaches founded on the developed q-ROFWGMSM and q-ROFWGGMSM operators. The developed approaches are more general and more practicable than Liu and Wang's MADM approach (2018), Wei and Lu's MADM method (2017), Qin and Liu's MADM method (2014) and Shen et al.’s MADM approach (2018).

Induction approach via P-Graph to rank clean technologies

Identification of appropriate clean technologies for industrial implementation requires systematic evaluation based on a set of criteria that normally reflect economic, technical, environmental and other aspects. Such multiple attribute decision-making (MADM) problems involve rating a finite set of alternatives with respect to multiple potentially conflicting criteria. Conventional MADM approaches often involve explicit trade-offs in between criteria based on the expert's or decision maker's priorities. In practice, many experts arrive at decisions based on their tacit knowledge. This paper presents a new induction approach, wherein the implicit preference rules that estimate the expert's thinking pathways can be induced. P-graph framework is applied to the induction approach as it adds the advantage of being able to determine both optimal and near-optimal solutions that best approximate the decision structure of an expert. The method elicits the knowledge of experts from their ranking of a small set of sample alternatives. Then, the information is processed to induce implicit rules which are subsequently used to rank new alternatives. Hence, the expert's preferences are approximated by the new rankings. The proposed induction approach is demonstrated in the case study on the ranking of Negative Emission Technologies (NETs) viability for industry implementation.

Dam construction material selection by implementing the integrated SWARA–CODAS approach with target-based attributes

The selection of the optimal material in engineering design procedures of any construction project due to the complexity of future circumstances could be considered as a complex problem. Similar complex selection problems can be efficiently implemented with the support of Multiple Attribute Decision Making (MADM) methods. The traditional MADM methods focus both on beneficial or non-beneficial attributes to determine a rank order of feasible alternatives and to select the best one. Nevertheless, like many engineering design problems, some attributes could be assessed based on target values. Therefore, target values of attributes along with beneficial and non-beneficial attributes makes a decision-making method more robust. Despite practical and functional applications of the target-based MADM approaches particularly in engineering design problems, only a few studies have made attempts to implement such methods. The presented study tackles a material selection problem by applying a hybrid decision-making approach supported on the Step-Wise Weight Assessment Ratio Analysis (SWARA) method and COmbinative Distance-based ASsessment (CODAS) technique containing target-based attributes. A case-study concerning the selection of optimal cement material type based on a real-world conceptual dam construction project in Iran has been analyzed by the proposed method considering two categories of attributes, i.e., managerial issues and technical specifications.

RANKING RANGE BASED APPROACH TO MADM UNDER INCOMPLETE CONTEXT AND ITS APPLICATION IN VENTURE INVESTMENT EVALUATION

In real-world Multiple Attribute Decision Making (MADM) problem, the attribute weights information may be unknown or partially known. Several approaches have been suggested to address this kind of incomplete MADM problem. However, these approaches depend on the determination of attribute weights, and setting different attribute weight vectors may result in different ranking positions of alternatives. To deal with this issue, this paper develops a novel MADM approach: the ranking range based MADM approach. In the novel MADM approach, the minimum and maximum ranking positions of every alternative are generated using several optimization models, and the average ranking position of every alternative is produced applying the Monte Carlo simulation method. Then, the minimum, maximum and average ranking positions of the alternative are integrated into a new ranking position of the alternative. This novel approach is capable of dealing with venture investment evaluation problems. However, in the venture investment evaluation process, decision makers will present different risk attitudes. To deal with this issue, two ranking range based MADM approaches with risk attitudes are further designed. A case study and a simulation experiment are presented to show the validity of the proposal.

Charting the Future of Life Cycle Sustainability Assessment: A Special Issue

Charting the Future of Life Cycle Sustainability Assessment: A Special Issue

Multiple Attribute Decision-Making Method Using Correlation Coefficients of Normal Neutrosophic Sets

The normal distribution is a usual one of various distributions in the real world. A normal neutrosophic set (NNS) is composed of both a normal fuzzy number and a neutrosophic number, which a significant tool for describing the incompleteness, indeterminacy, and inconsistency of the decision-making information. In this paper, we propose two correlation coefficients between NNSs based on the score functions of normal neutrosophic numbers (NNNs) (basic elements in NNSs) and investigate their properties. Then, we develop a multiple attribute decision-making (MADM) method with NNSs under normal neutrosophic environments, where, by correlation coefficient values between each alternative (each evaluated NNS) and the ideal alternative (the ideal NNS), the ranking order of alternatives and the best one are given in the normal neutrosophic decision-making process. Finally, an illustrative example about the selection problem of investment alternatives is provided to demonstrate the application and feasibility of the developed decision-making method. Compared to the existing MADM approaches based on aggregation operators of NNNs, the proposed MADM method based on the correlation coefficients of NNSs shows the advantage of its simple decision-making process.

Piezoelectric material selection for transducers under fuzzy environment

Piezoelectric ceramics are extensively investigated materials for transducer application. The selection of optimal piezoelectric material for this particular application is a tedious task. It depends upon various physical properties, including piezoelectric charge coefficient (d33), electromechanical coupling factor (Kp), dielectric constant (ɛr), and dielectric loss (tanδ). The classical multiple attribute decision making (MADM) can be used for decision making if these properties are known precisely. However, these properties cannot be expressed by exact numerical values, since they are dependent upon the microstructure and fabrication process. Fuzzy-based MADM approaches can be helpful in such cases. In this paper, we have determined the ranks and rank indices (for degree of closeness) of important piezoelectric materials using fuzzy VlseKriterijumska Optimisacija I Kompromisno Resenje (VIKOR) technique. PLZT(8/65/35) ((Pb1−xLa x )(Zr y Ti1−y)O3) and KNN-LT-LS ((K0.44Na0.52Li0.04)-(Nb0.84Ta0.10Sb0.06)O3) consecutively are found to be the top-rank piezoelectric ceramics. This indicates that KNN-LT-LS can be used on behalf of lead-based piezo-ceramics.

Concurrent design of a nanoactuator for “X-abilities” using the multiple-attribute decision-making approach

Organizations are deploying well-designed nanoactuators supporting converged applications of defense, mechanical industry, biological applications, and so on. An attempt is made to integrate concurrent engineering and multiple-attribute decision-making approaches to design and develop nanoactuators for a number of abilities, for example, actuation, modelization, realization, and performance. Attributes corresponding to these abilities are identified using cause and effect diagram. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) that is suitable for optimum design of nanoactuators is selected. The method permits to consider concurrently all the attributes contributing to X-abilities. The method also permits to carry out sensitivity analysis of one or more attributes simultaneously and determine optimum values of parameters. The concurrent engineering and multiple-attribute decision-making method ensures that optimally selected nanoactuator elements are closest to the hypothetical best and farthest from the hypothetical worst solution in order to design nanoactuators for the application under consideration. Research methodology in the form of step-by-step procedure is implemented with the help of an illustrative example. An agenda has been set for further research using this methodology.

A comparative study on material selection for micro-electromechanical systems

Material selection is an important task because large numbers of competitive materials are available for various technological applications. Numerous material selection techniques are reported whereas most of the techniques are knowledge based and require performance indices for material selection. In this context, Ashby approach is one of the efficient methods which rely on performance indices of materials in specific application. However Multiple attribute decision making (MADM) approaches do not require exact physical relation for material selection. We have investigated micro-electromechanical system’s (MEMS) material selection using MADM approaches and compared their results with that of Ashby approach. Almost similar materials ranking indicates that MADM approaches are also efficient and ease to apply without any prior mathematical calculation for materials properties-application relation.

Multiple-attribute decision making methods for plant layout design problem

The layout design problem is a strategic issue and has a significant impact on the efficiency of a manufacturing system. Much of the existing layout design literature that uses a surrogate function for flow distance or for simplified objectives may be entrapped into local optimum; and subsequently lead to a poor layout design due to the multiple-attribute decision making (MADM) nature of a layout design decision. The present study explores the use of MADM approaches in solving a layout design problem. The proposed methodology is illustrated through a practical application from an IC packaging company. Two methods are proposed in solving the case study problem: Technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy TOPSIS. Empirical results showed that the proposed methods are viable approaches in solving a layout design problem. TOPSIS is a viable approach for the case study problem and is suitable for precise value performance ratings. When the performance ratings are vague and imprecise, the fuzzy TOPSIS is a preferred solution method.

A general multiple attribute decision-making approach for integrating subjective preferences and objective information

A general multiple attribute decision-making (MADM) approach is proposed to integrate subjective preferences and objective information and to assess the relative weights of attributes, where subjective preferences are expressed either by a fuzzy preference relation on decision alternatives or by a pairwise comparison matrix on the relative weights of attributes or by both of them and objective information is expressed by a decision matrix. Three special cases of the approach are investigated, which are the weighted least deviation norm (WLDN), the weighted least-square deviation norm (WLSDN) and the weighted minimax deviation norm (WMDN) approaches. The extension of the general approach to group decision-making and the choice of decision parameters are also discussed. A numerical example is examined to show the applications of the proposed MADM approaches.