Multi-attribute Decision-making Model Research Articles

商业航天投资效益优化的群体多属性决策模型

商业航天投资效益优化的群体多属性决策模型

Hybrid weighted aggregation operator of cubic fuzzy-consistency elements and their group decision-making model in cubic fuzzy multi-valued setting

The ambiguity and uncertainty of human cognition about actual engineering problems are very challenging and indispensable issues in the information expression and aggregation. However, existing various cubic (hesitant) concepts may not reasonably represent the hybrid information of both an interval-valued fuzzy value and a fuzzy sequence with identical and/or different fuzzy values, which commonly occurs in engineering fields. To express the hybrid information, this paper first proposes the notion of a cubic fuzzy multi-valued set as a new extension of existing cubic (hesitant) notions and defines operational relations of cubic fuzzy multi-valued elements. To obtain reasonable operations between different fuzzy sequence lengths in cubic fuzzy multi-valued elements, cubic fuzzy multi-valued elements are transformed into cubic fuzzy-consistency elements based on the average value and consistency degree/level (complement of standard deviation) of a fuzzy sequence in a cubic fuzzy multi-valued element. Next, we present operations of cubic fuzzy-consistency elements and an expected value of a cubic fuzzy-consistency element for ranking cubic fuzzy-consistency elements. Further, we propose a cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator, and then develop a multi-attribute group decision-making model using the cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator and expected value of cubic fuzzy-consistency elements to solve group decision-making problems under the cubic fuzzy multi-valued environment. To reflect the feasibility and effectiveness of the developed group decision-making model, the developed group decision-making model is utilized in an example on the selection problem of slope design schemes regarding an open pit mine in the cubic fuzzy multi-valued environment. Comparative analysis indicates the flexibility and rationality of the developed group decision-making model.

Evaluating smart office buildings from a sustainability perspective: A model of hybrid multi-attribute decision-making

This study proposes a framework that contains four dimensions (environment, economy, socio-cultural, and technology) and nine relevant criteria for evaluating smart office buildings through a hybrid multi-attribute decision-making (MADM) model. A Taiwanese case study of the silver-level label office building is evaluated within this study. Using the Decision making trial and evaluation laboratory (DEMATEL) (di + ri = central degree, di-ri = cause degree) this study reveals that the core dimension for evaluating smart office buildings more sustainable is the technological dimension (D4) (34.007, 0.967), followed by the economic dimension (D2) (38.725, 0.175) and the socio-cultural dimension (D3) (37.743, 0.047); improvements to these three dimensions drives the environmental dimension (D1) (48.694, −1.190). In terms of the criteria, automation (C8) (19.328, 0.587), interactivity (C9) (14.679, 0.380), waste management (C3) (13.884, 0.300), architectural innovation (C7) (18.882, 0.226) and affordability (C4) (19.070, 0.201) are five critical criteria. Although the performance of the Taiwanese case study is, in terms of its sustainability, acceptable (Total performance = 9.530 and the gap ratio to aspiration level = 0.046) based on the modified VlseKriterjumska Optimizacija i Kompromisno Resenje (VIKOR), there is still room for further improvements to be made in terms of sustainability.

Discovery of key function module in complex mechanical 3D CAD assembly model for design reuse

PurposeComplex mechanical 3D computer-aided design (CAD) model embodies rich implicit design knowledge. Through discovering the key function parts and key function module in 3D CAD assembly model in advance, it can promote the designers’ understanding and reuse efficiency of 3D assembly model in design reuse.Design/methodology/approachAn approach for discovering key function module in complex mechanical 3D CAD assembly model is proposed. First, assembly network for 3D CAD assembly model is constructed, where the topology structure characteristics of 3D assembly model are analyzed based on complex network centrality. The degree centrality, closeness centrality, betweenness centrality and mutual information of node are used to evaluate the importance of the parts in 3D assembly model. Then, a multi-attribute decision model for part-node importance is established, and the comprehensive evaluation for key function parts in 3D assembly model is accomplished by combining Analytic Hierarchy Process and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Subsequently, a community discovery of function module in assembly model-based Clauset–Newman–Moore (CNM)-Centrality is given in details. Finally, 3D CAD assembly model of worm gear reducer is taken as an example to verify the effectiveness and feasibility of proposed method.FindingsThe key function part in CAD assembly model is evaluated comprehensively considering assembly topology more objectively. In addition, the key function module containing key function part is discovered from CAD assembly model by using CNM-Centrality-based community discovery.Practical implicationsThe approach can be used for discovering important design knowledge from complex CAD assembly model when reusing the assembly model. It can help designers capture and understand the design thinking and intent, improve the reuse efficiency and quality.Originality/valueThe paper first proposes an approach for discovering key function module in complex mechanical 3D CAD assembly model taking advantage of complex network theory, where the key function part is evaluated using node centrality and TOPSIS, and the key function module is identified based on community discovery.

Integrating Self-Report and Psychophysiological Measures in Waterpipe Tobacco Message Testing: A Novel Application of Multi-Attribute Decision Modeling.

Background: Waterpipe (i.e., hookah) tobacco smoking (WTS) is one of the most prevalent types of smoking among young people, yet there is little public education communicating the risks of WTS to the population. Using self-report and psychophysiological measures, this study proposes an innovative message testing and data integration approach to choose optimal content for health communication messaging focusing on WTS. Methods: In a two-part study, we tested 12 WTS risk messages. Using crowdsourcing, participants (N = 713) rated WTS messages based on self-reported receptivity, engagement, attitudes, and negative emotions. In an in-lab study, participants (N = 120) viewed the 12 WTS risk messages while being monitored for heart rate and eye-tracking, and then completed a recognition task. Using a multi-attribute decision-making (MADM) model, we integrated data from these two methods with scenarios assigning different weights to the self-report and laboratory data to identify optimal messages. Results: We identified different optimal messages when differently weighting the importance of specific attributes or data collection method (self-report, laboratory). Across all scenarios, five messages consistently ranked in the top half: four addressed harms content, both alone and with themes regarding social use and flavors and one addiction alone message. Discussion: Results showed that the self-report and psychophysiological data did not always have the same ranking and differed based on weighting of the two methods. These findings highlight the need to formatively test messages using multiple methods and use an integrated approach when selecting content.

Trusted Network Evaluation Model Based on Comprehensive Trust

Trusted network is currently evaluated based on the trustworthiness of the nodes in the network; thus, trust in the nodes is required to achieve trust in the network. The methods for calculating node trust mainly utilize the following three types of models: node behavior model, multiple-attribute decision-making model and reputation model. User behavior model always determines trust values based on harmful node behaviors. Multi-attribute decision-making model can be regarded as refinements of node behavior model. The trust values of the nodes are determined based on compromised trust attribute data and not just harmful behavior data. Reputation model calculates the trust values of nodes using subjective evaluation data for nodes. This paper presents a comprehensive trust model that combines subjective evaluation data and objective attribute data to calculate node trust. The experimental results show that malicious nodes can be effectively identified, which improves the service success rate of the system.

An integrated entropy-based multi-attribute decision-making model for phase change material selection and passive thermal management

The growing environmental concerns related to fossil-based power usage have led to a global transition towards renewable and conservative energy usage practices. In the context of human thermal comfort within enclosures, Phase Change Materials (PCMs) are deployed as effective passive conditioning media to curtail active air-conditioning demands. The successful functioning of PCM is strongly dependent upon a match between its thermophysical properties and the working environment’s thermal attributes. This creates a multifaceted selection scenario, given the availability of PCMs with a wide range of thermophysical properties. In this study, we investigate the case of selecting suitable PCM for passive thermal management in an automobile cabin. Various decision strategies and analytical methods are implemented to evaluate the efficacy of PCM selection using Multiple Objective Decision-Making (MODM) and Multiple Attribute Decision-Making (MADM) approaches. This study integrates different approaches and provides a comparative analysis of the selection processes. MODM is used for objectively evaluating the attributes (or properties of the PCMs) with Ashby’s approach. The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used along with the entropy-based weights. The selection algorithms suggest the suitability of Gallium and a few other PCMs for the desired application. This study could be a precursor to experimental and computational fluid dynamics (CFD)-based investigations for problems where the test set of PCMs could be narrowed down in the initial stage to save subsequent analysis time and money.

From Topology Optimization to Complex Digital Architecture: A New Methodology for Architectural Morphology Generation

Owing to the capacities of generating structural configuration with both reasonable mechanical properties and high material utilization, topology optimization has been widely adopted in engineering design. Although numerous architects have tried to apply topology optimization tools to assist architectural morphology design in practical projects, topology optimization, like other quantitative analysis techniques, has not been systematically incorporated into the architectural morphology design. In this study, by integrating topology optimization toolsets and parametric design theory, combined with multiattribute decision-making analysis, a design method is proposed that could efficiently obtain several architectural structural architectural morphologies with both structural rationality and aesthetic rules and complete the evaluation and performance ranking of alternatives. In this study, the essential architectural application scenarios are divided into surface application scenarios and volumetric application scenarios, and the possible variation range of topology optimization parameters of architectural application scenarios is defined. By iteratively adjusting the influence parameters, diverse results of structural morphology are obtained. It is found that small changes in optimization parameters will bring great differences in topological results. Such a sensitive relationship can be utilized to generate a set of rational topological structures, and these topological results can be regarded as alternatives for architectural morphology design. For the performance evaluation and ranking analysis of alternatives, the application of FANP-TOPSIS multiattribute decision-making model is put forward in this study. The case study shows that this decision-making analysis model is efficient, convenient, and applicable in the architectural morphology design. The results of this study can provide new ideas and key references for scholars and architects in the field of architecture to explore the process and method of architectural morphology design and other related issues.

Multiattribute Decision-Making Problems in terms of the Weighted Mean Operation of Two Aggregation Operators of Orthopair Z-Numbers

The Z number defined by Zadeh can depict the fuzzy restriction/value and reliability measure by an ordered pair of fuzzy values to strengthen the reliability of the fuzzy restriction/value. However, there exist truth and falsehood Z-numbers in real life. Thus, the Z number cannot reflect both. To indicate both, this study presents an orthopair Z-number (OZN) set to depict truth and falsehood values (intuitionistic fuzzy values) and their reliability levels in uncertain and incomplete cases. Next, we define the operations, score and accuracy functions, and sorting rules of OZNs. Further, the OZN weighted arithmetic mean (OZNWAM) and OZN weighted geometric mean (OZNWGM) operators are proposed based on the operations of OZNs. According to the weighted mean operation of the OZNWAM and OZNWGM operators, a multiattribute decision-making (MADM) model is established in the case of OZNs. Lastly, a numerical example is presented to reflect the flexibility and rationality of the presented MADM model. Comparative analysis indicates that the presented MADM model can indicate its superiority in the reliability and flexibility of decision results. Meanwhile, the resulting advantage of this study is that the presented MADM model can strengthen the reliability level of orthopair fuzzy values and make the decision results more reliable and flexible.

A multi-attribute approach to select the best method for repairing river bridge columns

PurposePier scour is one of the main causes of damage to the columns of the river bridges. It is essential to select the best method among various repair methods based on different evaluation indices. However, there is no procedure for ranking these repair methods based on their attributes. The present study seeks to set an approach for this ranking.Design/methodology/approachIn this paper, a multi-attribute decision-making (MADM) model is presented for ranking the repair techniques, in which alternatives are examined using the most important evaluation criteria. In addition, a combination of entropy and eigenvector methods has been proposed for weighting these attributes. A case study is then used to demonstrate the applicability and the validity of the method.FindingsThe execution of the model using two multi-criteria methods yielded similar results, which confirms its accuracy and precision. Moreover, the research findings showed the consistency of the objective and subjective weighting methods and the conformity of the weights obtained for the attributes from the combination of these methods to the nature of the problem.Originality/valueThe selection of the proper method for repairing the bridge columns plays an essential role in success of the bridge restoration. The proposed model introduces an approach for ranking repair methods and selecting the best one that has not been presented so far. Also, the weighing method for attributes is an innovative method for ranking restoration methods that has been proven in a case study.

Associated Probabilities in Interactive MADM under Discrimination q-Rung Picture Linguistic Environment

In some multi-attribute decision-making (MADM) models studying attributes’ interactive phenomena is very important for the minimizing decision risks. Usually, the Choquet integral type aggregations are considered in such problems. However, the Choquet integral aggregations do not consider all attributes’ interactions; therefore, in many cases, when these interactions are revealed in less degree, they do not perceive these interactions and their utility in MADM problems is less useful. For the decision of this problem, we create the Choquet integral-based new aggregation operators’ family which considers all pair interactions between attributes. The problem under the discrimination q-rung picture linguistic and q-rung orthopair fuzzy environments is considered. Construction of a 2-order additive fuzzy measure (TOAFM) involves pair interaction indices and importance values of attributes of a MADM model. Based on the attributes’ pair interactions for the identification of associated probabilities of a 2-order additive fuzzy measure, the Shapley entropy maximum principle is used. The associated probabilities q-rung picture linguistic weighted averaging (APs-q-RPLWA) and the associated probabilities q-rung picture linguistic weighted geometric (APs-q-RPLWG) aggregation operators are constructed with respect to TOAFM. For an uncertainty pole of experts’ evaluations on attributes regarding the possible alternatives, the associated probabilities of a fuzzy measure are used. The second pole of experts’ evaluations as arguments of the aggregation operators by discrimination q-rung picture linguistic values is presented. Discrimination q-rung picture linguistic evaluations specify the attribute’s dominant, neutral and non-dominant impacts on the selection of concrete alternative from all alternatives. Constructed operators consider the all relatedness between attributes in any consonant attribute structure. Main properties on the rightness of extensions are showed: APs-q-RPLWA and APs-q-RPLWG operators match with q-rung picture linguistic Choquet integral averaging and geometric operators for the lower and upper capacities of order two. The conjugation among the constructed operators is also considered. Connections between the new operators and the compositions of dual triangular norms (Tp,Spq) and (Tmin,Smax) are also constructed. Constructed operators are used in evaluation of a selection reliability index (SRI) of candidate service centers in the facility location selection problem, when small degree interactions are observed between attributes. In example MADM, the difference in optimal solutions is observed between the Choquet integral aggregation operators and their new extensions. The difference, however, is due to the need to use indices of all interactions between attributes.

A three-way decision approach with probabilistic dominance relations under intuitionistic fuzzy information

As a complicated cognitive process, multi-attribute decision-making usually focuses on the decision-making issue of seeking the optimal alternative or ranking alternatives under the framework of multiple attributes. The three-way decision approach with the delayed decision can more effectively reduce decision risks than traditional two-way counterparts for multi-attribute decision-making. In this article, we aim to put forward a novel three-way multi-attribute decision-making model in light of a probabilistic dominance relation with intuitionistic fuzzy sets. First, we investigate the three-way multi-attribute decision-making in light of a probabilistic dominance relation in an intuitionistic fuzzy information system. Second, we derive the conditional probability of the intuitionistic fuzzy set. Third, we evaluate the part supplier selection via the constructed model. At last, for the sake of showing the validity and applicability of the constructed three-way intuitionistic fuzzy multi-attribute decision-making model, we further perform extensive comparative analysis along with experimental analysis from diverse perspectives.

A multi-attribute group decision model based on unbalanced and multi-granular linguistic information: An application to assess entrepreneurial competencies in secondary schools

Advances in multi-attribute group decision making require the development of structures flexible enough to deal with unbalanced and multi-granular linguistic information. New distances between linguistic terms are needed to aggregate opinions and measure consensus among decision makers with different profiles. In this paper, firstly, based on the lattice structure of hesitant fuzzy linguistic terms sets, a perceptual-based distance able to capture differences between unbalanced linguistic assessments is developed. Secondly, a projected algebraic structure is defined to deal with multi-perceptual group decision-making contexts where each decision maker has its own qualitative reasoning approach. To this end, a methodology to aggregate unbalanced linguistic information based on different perceptual maps is developed. This methodology can also deal with different multi-granularity linguistic environments. Finally, through an illustrative example based on real data provided by the Andorra Government in a pilot test, the proposed framework is applied to classify and rank a set of secondary students according to their degree of entrepreneurial competency.

Multi-Attribute Decision-Making Approach for a Cost-Effective and Sustainable Energy System Considering Weight Assignment Analysis

The need for inexpensive and sustainable electricity has become an exciting adventure due to the recent rise in the local population and the number of visitors visiting the Banana Islands. Banana Islands is a grid-isolated environment with abundant renewable energy, establishing a hybrid renewable energy-based power system may be a viable solution to the high cost of diesel fuel. This paper describes a dual-flow optimization method for electrifying the Banana Islands, a remote island in Sierra Leone. The study weighs the pros and cons of maintaining the current diesel-based power setup versus introducing a hybrid renewable energy system that takes backup component analysis into account. Hybrid Optimization of Multiple Energy Resources (HOMER) software is used in the first optimization to optimally design the various system configurations based on techno-economic and environmental characteristics. A Multi-Attribute Decision-Making (MADM) Model that takes into account in the second optimization, the Combinative Distance-based Assessment System (CODAS) algorithm, and various methods of assigning weights to the attributes is used to rank the best configuration. The results show that the hybrid renewable energy system is a better option for electrifying the Banana Islands than the current stand-alone system. The Analytical Hierarchy Process (AHP) method of weight assignment was found to be superior to the Entropy method. Biogas generator-assisted hybrid configurations outperformed diesel generator-assisted hybrid configurations. With an optimum design of 101 kW PV, 1 wind turbine, 50 kW biogas, 86 batteries, and a 37.8 kW converter, the PV-wind-biogas-battery system is rated as the best configuration. It has a net present cost (NPC) of $487,247, a cost of energy (COE) of $0.211/kWh, and CO2 emission of 17.5 kg/year. Sensitivity analyses reveal that changes in the rate of inflation and the cost of storage have a significant effect on the overall cost of the configuration.

Perancangan Decision Support System Dengan Metode TOPSIS Untuk Pemilihan Bahan Baku Pada Perusahaan Manufaktur

The development of information system technology has penetrated the decision support system. Decision support systems can be applied in the selection of raw materials in manufacturing companies. The standard quantity of raw materials is the quantity of raw materials that should be used in the management of a particular product. Determining the standard of raw material quality starts from the application of product specifications, both regarding size, shape, color, characteristics, product processing and quality. Decision Support System (DSS) or Decision Support System (DSS) is a system which is able to provide alternatives in problem solving or can communicate for problems with semi-structured or unstructured conditions. This system is used to assist decision making in semi-structured and unstructured situations, where no one knows for sure how decisions should be made. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a concept where the best chosen alternative not only has the shortest distance from the positive ideal solution, but also has the longest distance from the negative ideal solution. This concept is widely used in several MADM (Multi Attribute Decision Making) models to solve practical decision problems. Research development uses the R & D (Research and Development) method which is a research method used to produce certain products and test certain products. In this study, only 6 steps were used. The total score for validation is 28. Based on the above calculation, it can be concluded that the validation results from the experts show a value of 4.0 which is between 3.26 - 4.0 which is classified as a very valid category.

A Novel Three-Way Decision Model Based on Utility Theory in Incomplete Fuzzy Decision Systems

The existing three-way decision (TWD)-making methods cannot effectively handle incomplete multiattribute decision-making (MADM) problems in real life; it is necessary to explore an effective three-way MADM model in incomplete fuzzy decision systems (IFDSs). First, we consider the preference of decision makers for each alternative and introduce the concept of predecisions, thus an IFDS is obtained. Then, the weighted conditional probabilities are calculated with the aid of the defined similarity relation. Subsequently, we introduce the notion of relative utility functions and then present an approach to determine the relative utility function values. Afterward, we construct a TWD model in IFDSs and apply it to the modeling of incomplete MADM (IMADM) problems. Our study not only enriches TWD and MADM theories but also provides a new perspective for realistic IMADM problems. At last, the results of comparative and experimental analyses demonstrate the validity, stability, and superiority of our proposed model.

Evaluation of education scenarios for acquiring digital competences of secondary school students in Slovenia

Digital competences are among the most important competences in modern society, with the education system playing a key role in the acquisition of digital competences by youths. As a rule, digital competences of students in general secondary schools in the EU are not optimal and vary considerably. As EU states are autonomous regarding secondary schools, their education organisation varies. Therefore, attempts are being made to implement certain standards of learning outcomes to be attained by secondary school students. Such standards are being prepared, and systematic implementation of the acquisition of digital competences in schools will begin as soon as there are enough trained teachers, for who in 2017 in the framework of DigCompEdu, a European framework of digital competences of educators was developed, with concrete advice for their work in the education process. We have prepared several education scenarios that can be used to enhance digital competences of students in general secondary schools where there are no professional subjects in which the students could acquire digital competences. Since the education scenarios are described by a large number of parameters, we prepared a hierarchical multi-attribute decision model for their evaluation and grading. Experts from the ministry, from professional services and secondary schools, cooperated in the preparation and validation of the model. The model is an aid to decision-makers who are looking for best scenarios, and also allows schools to evaluate their status and recognise what has to be done to attain a certain level of digital competences of students.

Application of quantum-like Bayesian network and belief entropy for interference effect in multi-attribute decision making problem

Multi-attribute decision making (MADM) is a classical model that widely used in various fields. But most research about this topic often assume that the attributes are independent each other. In fact the attributes maybe interact or interference each other in many cases. There are researches on the interact relationships with Choquet integral, but there is no research on the interference effect by far to the authors. In this paper, a multi-attribute decision-making model is proposed by considering the interference effects between attributes with quantum-like Bayesian network (QLBN) and belief entropy. Quantum probability theory (QPT) is more in line with people's thinking by considering the interference relationships between attributes, which is represented as the interference value in quantum theory. Belief entropy, an index to calculate the uncertainty of probability, is introduced as a method to calculate the interference value in this paper. Finally, the model is applied to assessing the industrial economic benefit as well as the quantitative performance comparison between the traditional probability and the QLBN probability is presented. It shows that the combination of QLBN model and belief entropy has prediction ability compared with the traditional Bayesian network model.

Multiple Attribute Decision-Making Model for Supplier Selection in Service-Oriented Manufacturing Paradigm

This paper proposes a multiattribute decision-making model for supplier selection under the service-oriented manufacturing, which can be used to effectively evaluate each candidate supplier. The supplier selection index system under the service-oriented manufacturing is proposed, and the interval evaluation matrix is established. In view of the mixed attribute of evaluation index, we construct a method that converts mixed attribute value to interval number. In order to avoid the subjectivity of the weight and make alternatives be provided with more discrimination, we use a combination model based on the deviation function model and the interval relative entropy ranking method to evaluate each candidate supplier. Finally, an application example is given to verify the correctness and practicability of the proposed decision-making model.

Modeling Interactive Multiattribute Decision-Making via Probabilistic Linguistic Term Set Extended by Dempster–Shafer Theory

In multiattribute decision-making (MADM), more and more attention is paid to the interaction between attributes when considering the actual decision environment. As a result, interactive MADM has become an emerging and challenging area of research whose success will greatly facilitate the development of decision-making. This paper models the interactive MADM problem and its contribution is multifaceted. First, the concept of probabilistic linguistic term set (PLTS) is extended by Dempster–Shafer theory (DST), which helps to express more uncertain information, followed by some basic operations, such as score function and aggregation operator. In virtue of evidential best-worst method and the principle of maximum entropy, then a novel nonadditive measure determination method is developed based on the 2-order additive measure to better model the interaction between attributes. Further, the generalized PLTS-based Choquet integral is defined by which generalized PLTSs on a nonadditive measure can be reasonably aggregated. Finally, an interactive MADM model is constructed and the technical details are described. The proposed approach is implemented to select the supplier for medical devices, and its effectiveness is emphasized by comparison with other methods.