Multi-attribute Decision-making Model Research Articles

Weighted-additive fuzzy multi-choice goal programming (WA-FMCGP) for supporting renewable energy site selection decisions

This paper proposes a novel weighted-additive fuzzy multi-choice goal programming (WA-FMCGP) model for the imprecise decision context wherein several conflicting goals are present but each goal has multiple-choice aspiration levels (MCALs) and, around them, the fuzzinesses are expressed in terms of membership functions (MFs). The main contribution of this model is its use of an objective function that minimises the weighted-additive summation of the normalised deviations; thus, the model can adopt any minimisation process from any goal programming (GP) variant. The advantages of this FGP-MCGP (fuzzy GP – multi-choice GP) model are shown by using it to solve a numerical example from F-MODM (fuzzy MODM) literature and comparing the results with those of a recent FP-MCGP (fuzzy programming – multi-choice GP) study. The application of the model is also verified using real data (i.e., it can model and support renewable energy site selection (RESS) where the decision context is imprecise). As WA-FMCGP is largely a MODM model, through its application, this study also provides a supplementary method in contrast to the multi-attribute decision-making (MADM) model applications used thus far for RESS.

Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks.

Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcome the energy consumption and network lifetime bottlenecks. The asynchronous working-sleeping cycle of sensor nodes could be exploited to make an opportunistic connection between sensor nodes in heterogeneous clustering. HFECS incorporates two levels of hierarchy in the network and energy heterogeneity is characterized using three levels of energy in sensor nodes. HFECS gathers local sensory data from sensor nodes and utilizes multi-attribute decision modeling and the entropy weight coefficient method for cluster formation and the cluster head election procedure. After cluster formation, HFECS uses the same techniques for performing data fusion at the first hierarchical level to reduce the redundant information flow from the first-second hierarchical levels, which can lead to an improvement in energy consumption, better utilization of bandwidth and extension of network lifetime. Our simulation results reveal that HFECS outperforms the existing benchmark schemes of heterogeneous clustering for larger network sizes in terms of half-life period, stability period, average residual energy, network lifetime, and packet delivery ratio.

Oil project selection in Iran: A hybrid MADM approach in an uncertain environment

This research focusses on the selection of oil projects by using Multi-Attribute Decision Making (MADM) methods in an uncertain environment. Oil production plays a crucial role in the economy of Iran, a country with many opportunities for onshore oil exploration. Differently from other countries, however, oil producers in Iran face some constraints with respect to oil extraction because some areas are shared with other producers. Additionally, oil producers in Iran must also allocate scarce physical, human, and monetary resources among different projects. Inaccurate decision-making may not only yield sub-optimal revenue generation, but also adversely affect the national economy For these reasons, priority oil projects must observe a sequence of steps. In the first step, critical factors for selecting oil projects are collected from previous related studies and experts are interviewed. These factors are subsequently filtered using the Delphi method. The oil projects are then ranked using a comprehensive approach involving novel alternative MADM methods. The best-worst method (BWM) is a new MADM stream that relies on pairwise comparison. It presents several distinct advantages with respect to fewer computational steps and higher discriminatory power among alternatives. Differently from previous research, this paper couples BWM with Weighted Aggregated Sum–Product Assessment (WASPAS) to improve result sensitivity under uncertain decision-making environments as modelled by Z-numbers. A robustness cross-check against other MADM models is also presented. Results indicate that quality has the highest priority and that production technology has the lowest priority among ten factors for oil project selection, thus reflecting the impact of US sanctions on oil production in Iran. Managerial implications and future avenues of research are derived.

Developing an Interactive Spatial Multi-Attribute Decision Support System for Assessing Water Resources Allocation Scenarios

In water resource management, assessing water resource allocation scenarios (WRASs) is an important multi-attribute decision making (MADM) problem. It involves spatially varied indicators, which interact with each other and impacts of the scenarios. These attributes are often simplified by using conventional Decision Support Systems (DSSs). In present research, a novel interactive spatial DSS for assessment of WRASs was developed. Effects of indicators type, decision matrix structures, and MADM models on priorities and ranks of scenarios were investigated in Aras basin. Sensitivity analysis of results showed that the interactive structure, comprising spatially distributed indicators and analytical network process (SANP), was the most stable model in terms of ranking. Providing more realistic results, the developed SDSS can be applied in other basins or for other MADM problems.

Acquisition of human capital for organizational sustainability: A BASS-SIR forecasting approach

We investigated the probabilities of acquisition of requisite human capital by new entrants in the institutional network. Graph theories applied in complex social networks using multi-attribute decision models were employed for our study. We coupled the dynamical and complexities of social network analysis with BASS-SIR epidemiological model to ascertain sustainability in the light of internal and external forces in the human capital acquisition. The results show that a new entrant’s ability to acquire human capital within the network increases rapidly and reaches a steady state by the third month. The fractional analysis indicated that a combined effect of internal and external injection of knowledge into the network sees entrants acquiring the required knowledge and work world skills in a relatively shorter time. Finally, the discrete and continuous analyses revealed that relatively even and continuous patterns of information flow reduce the time of reaching a steady state in the industry. We recommend a strong policy on continuous and even rates of training, a high rate of information flow such as internal and external training with the emphasis on internal training systems to gain competitive advantage.

Hybridized fuzzy analytic hierarchy process and fuzzy weighted average for identifying optimal design concept

In this article, a novel hybridized Multi-Attribute Decision Model (MADM) is developed to identify an optimal design of a Reconfigurable Assembly Fixture (RAF) from a set of alternative design concepts. The model combines the comparative advantage of Fuzzy Analytic Hierarchy Process (FAHP) and the computational strength of the Fuzzy Weighted Average (FWA) based on left and right scores in order to obtain aggregates for the design alternatives considering the relative importance of the design criteria as needed in the optimal design. The model was applied to evaluate four design concepts of a RAF with six design features having numerous sub-features. Results obtained from the evaluation process shows that there are differences in final values of the design alternatives. However, a close variation exists between these values. These differences can be accrued to the interrelationships between the design features and sub-features obtained from the Fuzzy Synthetic Extent (FSE) of the FAHP and an unambiguity judgment of the FWA when aggregating availability of the design features and sub-features in the design alternatives.

Research on situation awareness of network security assessment based on dempster-shafer

There are a lot of uncertainties in the network security situation assessment that depends on is multi-source and heterogeneous. Therefore, the objective uncertainties must be described and dealt with in the process of network security situation assessment. This paper proposes a multi-attribute decision-making trust evaluation model based on D-S evidence theory in multi-source and heterogeneous environment. By collecting, processing and evaluating attack event information from many data sources of security device, it can effectively evaluate the possibility of network intrusion, and provide a new exploration for network security situation assessment.

The q-Rung Orthopair Hesitant Fuzzy Uncertain Linguistic Aggregation Operators and Their Application in Multi-Attribute Decision Making

This paper combines the q-rung orthopair hesitant fuzzy sets (q-ROHFSs) with the uncertain linguistic variables, and proposes the q-rung orthopair hesitant fuzzy uncertain linguistic sets (q-ROHFULSs). In addition, the Schweizer-Sklar T-norm is introduced, and a multi-attribute decision-making method based on the q-rung orthopair hesitant fuzzy uncertain linguistic Schweizer-Sklar aggregation operators is established. Firstly, based on the Schweizer-Sklar T-norm, the operational properties of q-rung orthopair hesitant fuzzy uncertain linguistic elements are defined, and the score function, accuracy function and ranking method of the q-rung orthopair hesitant fuzzy uncertain linguistic elements are proposed. Secondly, the q-rung orthopair hesitant fuzzy uncertain linguistic Schweizer-Sklar Bonferroni mean (BM) operator and geometric Bonferroni mean (GBM) operator, Maclaurin symmetric mean (MSM) operator and dual Maclaurin symmetric mean (DMSM) operator, Maclaurin mean (MM) operator and dual Maclaurin mean (DMM) operator are defined. The calculation formulas of the operators are given, the related properties are studied, and the special forms of the operators are discussed. Finally, a multi-attribute decision-making model based on the q-rung orthopair hesitant fuzzy uncertain linguistic aggregation operators is established, and the feasibility and effectiveness of the decision-making method are demonstrated through calculation examples and comparative analyses.

Application of Incentive-Type Variable Weight in Decision of 500/220kV Received Electromagnetic Looped Grid Decomposing Operation

With the construction of 500kV grid, the 500/220kV electromagnetic looped grid of the received power gird will make short current problems stand out. Effectively and economically, considering the measure of the short current limitation, it is essential to decompose electromagnetic looped grid. Based on grid characteristics, grid operation arrangement should be optimized by strategy. Hence, the aspects, such as topological structure, security and stability, power supply reliability, operation economy and appropriate planning should be considered in decomposing 500/220kV electromagnetic looped grid and establishing evaluation index of the grid characteristics. With calculating constant weight by using maximizing deviation, the incentive-type variable weight multi-attribute decision model is proposed according to variable weight theory. This model guarantees objective analysis of the constant weight and extends discrimination of different potential schemes, simultaneously, the decision model’s effect will be improved from two perspectives of the index and the scheme. Last, based on evaluation index and decision model raised by this article, the real 500/200kV received electromagnetic looped grid will be decomposed and analyzed by different schemes, the accuracy and effectiveness of the conclusion obtained by incentive-type variable weight multi-attribute decision model is verified.

Dominance Degree Multiple Attribute Decision Making Based on Z-Number Cognitive Information

Cognitive information can be described by Z-number fully and effectively. However, many problems of Z-number need to be further studied. In this paper, two hidden probability models for calculating Z-number are established to provide more intuitive and abundant information. Next, the dominance degree relationship of Z-number is developed and subdivided. Furthermore, combined with the hidden probability of calculation, three different measurements of dominance degree are defined from three levels of geometry, algebra, and cross entropy based on the outranking relationship. The influencing factors are analyzed for different combinations of two probability models and three dominance degree measures. A multiattribute decision model is established on the basis of new grey association analysis and QUALIFLEX method. Finally, a decision example is given to verify the effectiveness and feasibility of the method. And sensitivity analysis is made to determine the impact of parameters and hidden probability on the decision model.

Dynamic BIM component recommendation method based on probabilistic matrix factorization and grey model

With rapid advances in building information modeling (BIM), a huge amount of BIM components has been built to increase design efficiency. Meanwhile, finding the appropriate BIM component in the huge library has become a challenge. Besides the methods of case-based reasoning (CBR) or multi-attribute decision model (MADM), the probabilistic matrix factorization (PMF) method of a recommendation system can be an efficient alternative. However, the user behavior patterns (i.e., the rating matrices) are changing with time to influence the recommendation precision. Therefore, this study aims to enhance the dynamic recommendation ability for BIM components by proposing a hybrid probabilistic matrix factorization method (PMF-GMn). The latent user preference matrix and the latent BIM component feature matrix can be generated by the PMF method from the rating matrix. Then, the predicted latent matrices can be obtained by the optimized grey model. Finally, the predicted latent matrices are further combined into the predicted rating matrix to recommend the appropriate BIM components. An illustrative example of the prefabricated building design is used to demonstrate the feasibility. This experiment is implemented by inviting twenty users to use the proposed SharePBIM platform for five months. The statistical results indicated that PMF-GMn can provide better performance than PMF in both two criteria of RMSE and Recall@k.

Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Identifying important nodes is very crucial to design efficient communication networks or contain the spreading of information such as diseases and rumors. The problem is formulated as follows: given a network, which nodes are the more important? Most current studies did not incorporate the structure change as well as application features of a network. Aiming at the node importance evaluation in wireless sensor networks, a new method which ranks nodes according to their structural importance and performance impact is proposed. Namely, this method considers two aspects of the network, network structural characteristics and application requirements. This method integrates four indicators which reflect the node importance, namely, node degree, number of spanning trees, delay, and network energy consumption. Firstly, the changes in the four indicators are analyzed using the node deletion method. Then, the TOPSIS multi-attribute decision-making method is applied to merge these four evaluation indicators. On this basis, a more comprehensive evaluation method (MADME) for node importance is obtained. Theory study reveals MADME method saves computational time. And the simulation results show the superiority of the MADME method over various algorithms such as the N-Burt method, betweenness method, DEL-Node method, and IE-Matrix method. The accuracy of the evaluation can be improved, and the key nodes determined by the MADME method have a more obvious effect on the network performance. Our method can provide guidance on influential node identification in the network.

A Novel Decision-Making Model with Pythagorean Fuzzy Linguistic Information Measures and Its Application to a Sustainable Blockchain Product Assessment Problem

This study presents a novel multi-attribute decision-making (MADM) model on the basis of Pythagorean fuzzy linguistic information measures. To do so, we first present a new concept of Pythagorean fuzzy linguistic sets to describe fuzziness and inconsistent information, in which the Pythagorean fuzzy linguistic values (PFLVs) are represented by the linguistic membership degree and linguistic non-membership degree. Then, we introduce two axiomatic definitions of information measures for PFLVs, including Pythagorean fuzzy linguistic entropy and the Pythagorean fuzzy linguistic similarity measure, to measure the uncertainty degree of PFLVs and the similarity degree between among PFLVs. In addition, based on the logarithmic function, we construct two new information measure formulas and verify that they satisfy the axiomatic conditions of the Pythagorean fuzzy linguistic entropy and similarity measure, respectively. We further explore the relationship between the Pythagorean fuzzy linguistic entropy and similarity measure. Finally, we present a novel Pythagorean fuzzy linguistic MADM model with the Pythagorean fuzzy linguistic entropy and similarity measure. A numerical example of selecting the most desirable sustainable blockchain product is given, and a comparison with the existing approach was performed to validate the reliability of the developed decision-making model.

Organic Vegetable Crops Managed with Agro-Ecological Practices: Environmental Sustainability Assessment by DEXi-met Decision Support System

In the last decade, there has been an increasing interest in sustainable agricultural techniques and the environmental evaluation of the effects of agricultural practices. In the present study, we evaluated both the production capacity of organic horticultural systems, and the ex-post sustainability through a new multi-attribute decision model named “DEXi-met”. This qualitative model is able to estimate the environmental sustainability of cropping systems managed with different agro-ecological approaches. In particular, we compared the following three horticultural systems: (i) ECO, an organic system with full implementation of agro-ecological strategies (agro-ecological services crops (ASC), strip cultivation, and organic amendment); (ii) GM, an organic system with the introduction of the ASC; (iii) NO ASC, an organic system without ASC. The treatments with ASC presence (ECO and GM) showed similar total energy outputs (substantially higher than the NO ASC), indicating the positive effect of this agro-ecological practice. The findings pointed out that the ECO system, which followed the principles of natural ecosystems, can contribute to building up more complex agro-ecosystems, increasing both resilience and biodiversity. This management strategy reached a good compromise between the production of vegetable cropping systems and environmental sustainability achievement. Then, it is possible to optimize the use of natural resources, support climate adaptation, and reduce greenhouse gas emissions.

A Novel FMEA Model Based on Rough BWM and Rough TOPSIS-AL for Risk Assessment

Failure mode and effects analysis (FMEA) is a risk assessment method that effectively diagnoses a product’s potential failure modes. It is based on expert experience and investigation to determine the potential failure modes of the system or product to develop improvement strategies to reduce the risk of failures. However, the traditional FMEA has many shortcomings that were proposed by many studies. This study proposes a hybrid FMEA and multi-attribute decision-making (MADM) model to establish an evaluation framework, combining the rough best worst method (R-BWM) and rough technique for order preference by similarity to an ideal solution technique (R-TOPSIS) to determine the improvement order of failure modes. In addition, this study adds the concept of aspiration level to R-TOPSIS technology (called R-TOPSIS-AL), which not only optimizes the reliability of the TOPSIS calculation program, but also obtains more potential information. This study then demonstrates the effectiveness and robustness of the proposed model through a multinational audio equipment manufacturing company. The results show that the proposed model can overcome many shortcomings of traditional FMEA, and effectively assist decision-makers and research and development (R&D) departments in improving the reliability of products.

Computation of generalized linguistic term sets based on fuzzy logical algebras for multi-attribute decision making

Computational models of various linguistic term sets whose evaluation scales are the identity functions have been extensively investigated. Among these models, although the virtual linguistic model is the most straightforward and convenient for the calculations, the operations are not closed on the given scales. In this paper, we develop the fuzzy logical algebras-based virtual linguistic model. We propose the generalized linguistic term sets based on general strictly increasing functions with the existing linguistic term sets as special cases, and define some novel operational laws and measures such as similarity measures, distance measures and entropy measures for them. Then we provide some linguistic aggregation operators for the generalized linguistic term sets in multi-attribute decision making. Particularly, when these operators are reduced to the existing ones, it is shown that the proposed operational laws can be considered as a modification of the existing ones in related literature. Based on the proposed operators, a multi-attribute decision-making model is built, and a method for determining the objective–subjective weight vector in generalized linguistic term sets are put forward. Finally, an illustrative example is presented for verifying our models and methods and a comparative analysis with the existing methods is made.

A Dempster‐Shafer theory and uninorm‐based framework of reasoning and multiattribute decision‐making for surveillance system

Closed-circuit television and sensor-based intelligent surveillance systems have attracted considerable attentions in the field of public security affairs. To provide real-time reaction in the case of a huge volume of the surveillance data, researchers have proposed event-reasoning frameworks for modeling and inferring events of interest. However, they do not support decision-making, which is very important for surveillance operators. To this end, this paper incorporate a function of decision-making in an event-reasoning framework, so that our model not only can perform event-reasoning but also can predict, rank, and alarm threats according to uncertain information from multiple heterogeneous sources. In particular, we propose a multiattribute decision-making model, in which an object being watched is modeled as a multiattribute event, where each attribute corresponds to a specific source, and the information from each source can be used to elicit a local threat degree of different malicious situations with respect to the corresponding attribute. Moreover, to assess an overall threat degree of an object being observed, we also propose a method to fuse the conflict threat degrees regarding all the relevant attributes. Finally, we demonstrate the effectiveness of our framework by an airport security surveillance scenario.

Data-driven hybrid multiple attribute decision-making model for green supplier evaluation and performance improvement

Abstract Multi-attribute decision-making (MADM) is the most commonly used methodology for green supplier evaluation and performance improvement. Previous MADM models have mainly relied upon the knowledge and opinions of domain-experts as the starting point for making decisions. However, the results are affected by the subjectivity of these judgments and knowledge limitations. This study develops a data-driven MADM model that utilizes potential rules/patterns derived from a large amount of historical data to help decision-makers objectively select suitable green suppliers and provide systemic improvement strategies to help reach the aspiration level. First, the random forest (RF) algorithm is applied to explore the pairwise influential strength relations among attributes derived from real audit data. The influence matrix derived using the RF algorithm is used as input for decision-making trial and evaluation laboratory (DEMATEL)-based analytical network process analysis which is carried out to obtain the influential strength weights of the attributes. Then, multi-objective optimization on the basis of ratio analysis to the aspiration level (MOORA-AS) is utilized to evaluate the gap between the current and aspiration levels for each green supplier. The developed critical influence strength route (CISR) can help managers derive various strategies for improving green supplier performance. The functioning of the proposed model is illustrated using data obtained from the green supplier management department of a Taiwanese electronics company. The results reveal that the proposed model can effectively help decision-makers to solve the problem of green supplier selection and devise strategies for improvement.

Assessment of Energy–Population–Urbanization Nexus with Changing Energy Industry Scenario in India

The demand for energy has been growing worldwide, especially in India partly due to the rapid population growth and urbanization of the country. To meet the ever-increasing energy requirement while maintaining an ecological balance is a challenging task. However, the energy industry-induced effect on population and urbanization has not been addressed before. Therefore, this study investigates the linkages between energy, population, and urbanization. The study also aims to find the quantifiable indicators for the population growth and rate of urbanization due to the expanding energy industry. The integrated framework uses a multi-temporal Landsat data to analyze the urbanization pattern, a census data for changes in population growth, night time light (NTL) data as an indicator for economic development and energy production and consumption data for energy index. Multi-attribute model is used to calculate a unified metric, termed as the energy–population–urbanization (EPU) nexus index. The proposed approach is demonstrated in the National Thermal Power Corporation (NTPC) Dadri power plant located in Uttar Pradesh, India. Landsat and NTL data clearly shows the urbanization pattern, economic development, and electrification in the study area. A comparative analysis based on various multi-attribute decision model assessment techniques suggests that the average value of EPU nexus index is 0.529, which significantly large compared to other studies and require special attention by policymakers because large EPU index indicates stronger correlation among energy, population, and urbanization. The authors believe that it would help the policymakers in planning and development of future energy projects, policies, and long-term strategies as India is expanding its energy industry.

Research on multi-attribute controller for virtual data domain based on software definition network

Currently, large data sets are deployed on large-scale clusters, which require a large amount of physical resources. However, current network architecture does not have flexible deployment, making it difficult to adjust physical resources after deployment. Based on software definition network, this article proposes a framework for building virtual data domain, which establishes a multi-attribute decision model by network nodes for optimizing the deployment of control layer, so as to realize large-scale deployment. By analyzing the actual usage and virtual distribution of the underlying data resources, a mapping algorithm of network resource overhead–allocation ratio is proposed for adjusting the mapping space of network flow based on the mapping result, so as to meet more virtual domain applications. At the same time, the reasonable utilization of resources is helpful to reduce the communication delay in domain. Simulation results show that compared with the shortest path mapping and greedy resource mapping algorithms, the virtual domain established by the network resource overhead–allocation algorithm can improve the resource utilization rate by 10% and reduce the intra-domain communication delay by 30%. Therefore, under the background of the expanding scale of data domain, this framework can solve the problem that the current backward network architecture cannot adapt to the development trend of the information field.