Fuzzy Decision-making Method Research Articles

Generalized orthopair fuzzy weighted distance‐based approximation (WDBA) algorithm in emergency decision‐making

With the intensification of global warming trends, the frequent occurrence of natural disasters has brought severe challenges to the sustainable development of society. Emergency decision-making (EDM) in natural disasters is playing an increasingly important role in improving disaster response capacity. In the case of EDM evaluation, the essential problem arises serious incompleteness, impreciseness, subjectivity, and incertitude. The q-rung orthopair fuzzy set (q-ROFS), disposing the indeterminacy portrayed by membership and nonmembership with the sum of qth power of them, is a more viable and effective means to seize indeterminacy. The aim of paper is to present a new score function of q-rung orthopair fuzzy number (q-ROFN) for solving the failure problems when comparing two q-ROFNs. Firstly, we introduce some basic set operations for q-ROFS. The properties of these operations are also discussed in detail. Later, we propose a q-rung orthopair fuzzy decision-making method based on weighted distance-based approximation (WDBA), in which the weights of decision-makers are obtained from a nonliner optimization model according to the deviation-based method. Finally, some examples are investigated to illustrate the feasibility and validity of the proposed approach. The salient features of the proposed method, compared to the existing q-rung orthopair fuzzy decision-making methods, are as follows: (a) it can obtain the optimal alternative without counterintuitive phenomena and (b) it has a great power in distinguishing the optimal alternative.

On generalized similarity measures for Pythagorean fuzzy sets and their applications to multiple attribute decision‐making

In this paper, we develop a new and flexible method for Pythagorean fuzzy decision-making using some trigonometric similarity measures. We first introduce two new generalized similarity measures between Pythagorean fuzzy sets based on cosine and cotangent functions and prove their validity. These similarity measures include some well-known Pythagorean fuzzy similarity measures as their particular and limiting cases. The measures are demonstrated to satisfy some very elegant properties which prepare the ground for applications in different areas. Further, the work defines a generalized hybrid trigonometric Pythagorean fuzzy similarity measure and discuss its properties with particular cases. Then, based on the generalized hybrid trigonometric Pythagorean fuzzy similarity measure, a method for dealing with multiple attribute decision-making problems under Pythagorean fuzzy environment is developed. Finally, a numerical example is given to demonstrate the flexibility and effectiveness of the developed approach in solving real-life problems.

Multi-Criteria Decision Making Based on Correlation Coefficient of Triangular Intuitionistic Fuzzy Numbers

We study a multi-criteria fuzzy decision-making method based on weighted triangular intuitionistic fuzzy number correlation coefficients. Under the scenario that criteria weights for alternatives are completely unknown, triangular intuitionistic fuzzy method can not only supplement the insufficiency of the method based on the distance but also endow more information to the estimation and reduce the loss of evaluation information. Among the triangular numbers, two boundary numbers are the maximum and minimum values of the interval respectively, and the medium number is the most possible value under subjective estimation. Using this method, we propose a new way to obtain the criteria weights with more information quantity. By ranking the relative closeness of the weighted correlation coefficients between each alternative, and the critical and ideal alternatives, we show the method to figure out the most suitable alternative based on the expected criteria. An illustrative example is also taken into account to prove the effectiveness of the model.

An efficient crowd-sourcing-based approach for fingerprint database updating

Fingerprint-based indoor localization has become one of the most attractive and promising techniques; however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially in the large-scale and long-term deployment. In this article, focusing on three key problems now existing in fingerprint database updating approaches such as the mechanism for triggering updates, the collection of new fingerprints and determination of fingerprints’ location information, we propose a fuzzy map mechanism and decision methods of neighbours’ fingerprints in response to all kinds of changes in indoor environments. Meanwhile, we design a static data collecting mechanism to filter reliable information from numerous users’ inputs and propose a neighbours’ fingerprint-assisted technique to calculate the location of fingerprints. Experimental results demonstrate that the proposed solution not only improves the performance of updating the fingerprint database in real time and robustness by 40% and 50%, respectively, but also reduces the update frequency and improves mean location accuracy by over 40%.

Hesitant Fuzzy Linguistic Archimedean Aggregation Operators in Decision Making with the Dempster–Shafer Belief Structure

We propose a novel method for hesitant fuzzy linguistic decision making by utilizing Dempster–Shafer (D–S) theory of evidence. First, we propose some novel operations of hesitant fuzzy linguistic term sets (HFLTSs) on the basis of closed operations on linguistic 2-tuples and distribution linguistic average aggregation (DLAA) operators. These novel operations not only avoid information loss and operational results exceeding the boundary of linguistic term sets, but also make the aggregation results interpretable. Then, we define the hesitant fuzzy linguistic Archimedean weighted arithmetic mean (HFLAWAM) and hesitant fuzzy linguistic Archimedean weighted geometric mean (HFLAWGM) operators of HFLTSs. These two proposed operators are able to overcome the shortcomings of the existing approaches to multicriteria decision making (MCDM) with HFLTSs. Then, to take into account the novel operations of HFLTSs and the MCDM under uncertainty, we propose the belief structure-HFLAWAM (BS-HFLAWAM) and belief structure-HFLAWGM (BS-HFLAWGM) operators of HFLTSs. After that, we create an approach to handle the hesitant fuzzy linguistic MCDM problems in light of proposed operators. Finally, we apply the created approach to a MCDM problem regarding political management of a country.

A collaborative design method for satellite module component assignment and layout optimization

The collaborative design of the multi-module satellite component (equipment) assignment and layout is the key aspect of the overall satellite design, and the two parts are closely related. In the past, satellite module component layout optimization usually adopted fixed component assignments, which remained constant in the layout optimization stage. If the components were improperly distributed in these modules, it would seriously affect the layout optimization. To overcome this disadvantage, a collaborative design method for the component assignment and layout design is presented for the multi-module (or multi-bearing plate) satellite component layout problem, based on a multi-agent system. First, the component assignment agent adopted a multi-objective optimization method (the non-dominated sorting genetic algorithm II, NSGA-II) to obtain the approximate Pareto solution set of the satellite component assignment scheme. Second, it adopted a fuzzy multi-objective decision method to select a high-quality component assignment scheme from the approximate Pareto solution set. Third, the layout agent employed a dual system co-evolutionary method for the layout optimization design. In the process of the layout optimization, the layout result is fed back to the component assignment design, and the component assignment is adjusted according to the result of the layout optimization. Thus, the above process is continually iterated to achieve the optimal collaborative design of the component assignment and the layout. The proposed method is applied to a simplified multi-module satellite component assignment and layout optimization problem and aims to provide a reference and technical support for other similar multi-module equipment assignment and layout optimization problems.

Similarity–divergence intuitionistic fuzzy decision using particle swarm optimization

Many existing intuitionistic fuzzy (IF) decision methods focus on a reasonable ranking for alternatives under unknown weight information. Traditionally, the weight information is usually determined from a multiobjective optimization model based on real-valued measures such as IF distance or similarity measures, which may lose divergence information. In this paper, we propose one new type of optimization model for determining the weights based on a fuzzy measure called the similarity–divergence measure (S–D measure). First, we develop similarity and divergence measures of IF sets respectively, and a 2-tuple consisting of similarity and divergence is defined as a S–D measure. This measure is further proven to be an IF similarity degree and has practical semantics of similarity and divergence features in human’s cognition. Second, we utilize such measure to calculate fuzzy similarities of each alternative and construct a nonlinear optimization model to determine the weights. Third, we design an algorithm for solving the model with the aid of particle swarm optimization and thus develop an IF decision method. Finally, two examples are given to demonstrate our method and then it is compared with existing methods to explain its effectiveness and superiority.

Covering-based general multigranulation intuitionistic fuzzy rough sets and corresponding applications to multi-attribute group decision-making

This paper concerns the intuitionistic fuzzy (IF) decision-making method for solving uncertain or vague problems. This traditional method has imperfections in some particular circumstances. For this reason, we combine rough set theory and the IF decision-making approach in order to design a novel procedure for making decisions. Several types of covering-based general multigranulation IF rough set (CGMIFRS) models are proposed by using four types of IF neighborhoods. A related example to illustrate these models is given. Furthermore, the relationships among the eight kinds of CGMIFRS models are also investigated. By the recourse to the principle of the PROMETHEE II method, a multi-attribute group decision-making (MAGDM) approach with IF information based on CGMIFRS models is set forth. A comparative analysis shows that the optimal objects of our method based on two IF logical operators, the IF PROMETHEE method and the IF aggregation operators are consistent. We conclude that our novel method is more effective to deal with MAGDM problems with IF information than the existing MAGDM methods.

Optimization modeling method for coal-to-electricity heating load considering differential decisions

Heating by electricity rather than coal is considered one effective way to reduce environmental problems. Thus, the electric heating load is growing rapidly, which may cause undesired problems in distribution grids because of the randomness and dispersed integration of the load. However, the electric heating load may also function as an energy storage system with optimal operational control. Therefore, the optimal modeling of electric heating load characteristics, considering its randomness, is important for grid planning and construction. In this study, the heating loads of distributed residential users in a certain area are modeled based on the Fanger thermal comfort equation and the predicted mean vote thermal comfort index calculation method. Different temperatures are considered while modeling the users’ heating loads. The heat load demand curve is estimated according to the time-varying equation of interior temperature. A multi-objective optimization model for the electric heating load with heat energy storage is then studied considering the demand response (DR), which optimizes economy and the comfort index. A fuzzy decision method is proposed, considering the factors influencing DR behavior. Finally, the validity of the proposed model is verified by simulations. The results show that the proposed model performs better than the traditional method.

Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method

One of methods for loss reduction and reliability improvement of radial distribution system is using of renewable energy generation. In this paper, a new optimal placement and sizing of renewable energy sources based on photovoltaic panels (PVs) and wind turbines (WTs) in the distribution network is presented with the objective of loss reduction and reliability improvement based on energy not-supplied (ENS). A multi-objective evolutionary algorithm based on fuzzy decision-making method, called the Multi-Objective Hybrid Teaching–Learning Based Optimization-Grey Wolf Optimizer (MOHTLBOGWO) is proposed to solve the optimization problem. The proposed hybrid method has a high convergence speed and not trapped at all in local optimal. The proposed method is implemented in the form of single-objective and multi-objective on 33 and 69 bus IEEE radial distribution networks. The simulation results clear that the multi-objective optimization is a more precise approach to network utilization taking into account all objective indices than the single objective method. The results show that the proposed method has better convergence speed and less convergence tolerance in achieving to best solution in comparison with TLBO and GWO methods in loss reduction, reliability improvement and increasing the net saving and also in comparison with last studies. Moreover, the results show that dispersion of the size and location of distributed renewable generation leads to a further reduction in losses and a better improvement of the reliability criterion.

Research on financial risk management model of internet supply chain based on data science

The supply chain financial risk management model is established under the Internet financial model, so as to improve the supply chain's ability and resist risks, a financial risk management model of Internet supply chain is proposed based on data science. Firstly, the forms and countermeasures of financial risk in Internet supply chain is analyzed. The data science analysis method is used to analyze the risk management model, and the supply chain financial risk management model is constructed based on the multivariate piecewise regression analysis and the financing decision effectiveness game. The relationship between supply chain financial risk management and enterprise performance is studied under the Internet finance model, the validity of the financing decision of Internet supply chain is analyzed, and it constructs the decision model of Internet supply chain by descriptive statistical analysis method. The fuzzy decision method is used to analyze the risk assessment of Internet supply chain, and the Simunic model is used to analyze the correlation model of risk management in Internet supply chain. In order to improve the risk management ability of Internet supply chain, the statistical analysis and comprehensive decision of risk control are carried out by using piecewise sample regression analysis. The results of empirical data analysis show that the model has good piecewise fit for financial risk management and management of supply chain under Internet financial model, and the accuracy of data evaluation is high. The model is robust for financial risk management and performance evaluation of supply chain.

Research on evaluation and optimization of sustainable indoor environment of public buildings in Xi’an in summer

ABSTRACT There have been more concerns over energy resource depletion and indoor comfort improvement along with the increased time spend in a building. Building operation requires more energy in the spaces of high occupancy density in public buildings in summer because most energy has been consumed to make the indoor environment comfortable. Therefore, there is a conflicting issue for optimization, which is minimum energy consumption vs. maximum indoor comfort. In this paper, the indoor comfort model was established based on the weights of the thermal environment and air quality. The indoor air temperature, indoor relative humidity and indoor CO2 concentration as KPI (Key performance indicators) are optimized dynamically according to different outdoor meteorological parameters and different indoor occupancy density by an improved multi-objective optimization method of Non-dominated Sorting Genetic Algorithm II (NSGA-II). A single suitable solution from the non-inferior solutions is selected by the method of fuzzy comprehensive decision. The single solution of those KPI can be used as the set point of air-conditioning controller. A case study was carried out and the corresponding simulation of optimization of energy and comfort was presented. The results showed that the methodology can achieve the maintenance of indoor comfort and energy consumption reduction.

A New Interval Type-2 Fuzzy Decision Method with an Extended Relative Preference Relation and Entropy to Project Critical Path Selection

Considering uncertainty in multi-criteria decision making (MCDM) is an important issue in today's business and management problems. In this article, to use advantages of IT2FSs, a novel interval type-2 fuzzy multi-criteria decision method is presented with an extended entropy and relative preference relation. To tackle vagueness and uncertainty of real-world problems, the IT2FSs are used and applied to a modified MCDM method. Furthermore, an entropy method is developed under an IT2F environment and for obtaining the final weight of each criterion, a relative preference relation is hybridized with an entropy method. Also, the weight of each decision maker (DM) is calculated by a new IT2F-order preference method by means of the relative closeness. Finally, an existing example about the project critical path selection by considering effective criteria, such as time, cost, quality and safety, is adopted from the literature and solved to indicate the capability of introduced method.

An Innovative Model to Choose E-Commerce Suppliers

Supplier selection for aquatic products E-commerce has aroused widespread concern due to the characteristics of fresh products and the popularity of the network. This paper proposes a novel fuzzy group decision method, which not only integrates quality function deployment (QFD) and improved technique for order preference by similarity to the ideal solution (TOPSIS) but also combines the qualitative analysis and quantitative analysis. Moreover, this method contains two stages, in the first stage, QFD is applied to determine the weights of customer requirements (CRs) and company strategies (CSs) by analyzing the relationship between CRs and CSs, the subjective importance weights of criteria can be derived, in addition, the house of quality (HOQ) is used to characterize the above process. To address the uncertainty in the selection process, intuitionistic fuzzy numbers (IFNs) are provided to describe the imprecise information, which take membership degree, non-membership degree and hesitation degree into consideration; in the second stage, a novel ranking method is proposed based on the improved TOPSIS, the objective importance weights of criteria are obtained, then the integrated importance criteria weights are calculated and a new ranking index is provided to rank the suppliers. What’s more, a case study illustrates the effectiveness and applicability of the proposed method. Finally, comparative analysis and sensitivity analysis are carried to demonstrate the robustness and effectiveness of the proposed method. The result shows that the proposed TOPSIS-based method can help companies monitor supplier performance and provide customers with quality products and services.

A Novel Fuzzy Decision-Making Method Using Entropy Weights-Based Correlation Coefficients Under Intuitionistic Fuzzy Environment

In this communication, a new two parametric intuitionistic fuzzy entropy has been introduced and validated. Some of the properties of the proposed measure are also discussed. In addition to this, a new multiple attribute decision-making (MADM) method based on weighted correlation coefficients and the proposed IF entropy is introduced. The necessity of proposed MADM method has been reasonably established. The method is effectively explained with the help of two numerical examples, and the performance is genuinely compared with some existing MADM methods in the literature. Attribute weights play an important role in the solution of MADM problem. In this paper, two methods of obtaining attributes weights are discussed.

New Operations on Generalized Hesitant Fuzzy Linguistic Term Sets for Linguistic Decision Making

Hesitant fuzzy linguistic term set is defined in hesitant fuzzy linguistic decision frameworks in which all decision makers are agreed on the primary linguistic scale and its membership functions; however, this situation does not always happen, because words mean different things to different people or there are some linguistic term sets with different semantics for different people. Hence, normalization of linguistic information is necessary. Inspired by normalizing linguistic information based on 2-tuple linguistic model, in this paper, the concept of generalized hesitant fuzzy linguistic term set is proposed when decision makers are not agreed on the primary linguistic scale and its membership functions, which is an extension of hesitant fuzzy linguistic term set. Then, t-norms and t-conorms are utilized to define new operations on generalized hesitant fuzzy linguistic term sets and their properties are discussed. Based on these new operations, the likelihood-based comparison relation of generalized hesitant fuzzy linguistic term sets is presented, which is an extension of the likelihood-based comparison relation of hesitant fuzzy linguistic term sets. Accordingly, the generalized hesitant fuzzy linguistic weighted average operator, generalized hesitant fuzzy linguistic weighted geometric operator, generalized hesitant fuzzy linguistic ordered weighted average operator and generalized hesitant fuzzy linguistic ordered weighted geometric operator are provided to fuse generalized hesitant fuzzy linguistic term sets in linguistic decision making. A case study is used to illustrate the practicality of the method based on generalized hesitant fuzzy linguistic term sets and compare with Lee and Chen’s method, Rodriguez’s method and Wei’s method. It seems that the method based on generalized hesitant fuzzy linguistic term sets is flexible and useful method for hesitant fuzzy linguistic decision making.

Performance Indicators Considered for Selection of Agency in Maritime Industry

Abstract A shipping agency or shipping agent is the designated person or agency held responsible for handling shipments and cargo, and the general interests of its customers, at ports and harbors worldwide, on behalf of ship owners, managers, and charterers. Shipping agents will usually take care of all the regular routine tasks of a shipping company quickly and efficiently. They ensure that essential supplies, crew transfers, customs documentation, and waste declarations are all arranged with the port authorities without delay and often provide the shipping company with updates and reports on activities at the destination port so that shipping companies have real-time information while goods are in transit. Due to these reasons, selection of the ship agencies becomes an indispensable part of maritime transportation. Managing a ship agency requires interconnected and labor-intensive processes for any kind of shipping operations, which results in a complicated mission. This study presents the most important ship agency selection criteria, according to the determined criteria in the literature, by employing fuzzy AHP technique, one of the most common fuzzy multiple criteria decision-making (MCDM) methods.

Ranking project success criteria in power engineering companies using fuzzy decision-making method

Ranking project success criteria in power engineering companies using fuzzy decision-making method

Exponential operation and aggregation operator for q-rung orthopair fuzzy set and their decision-making method with a new score function

q-Rung orthopair fuzzy set (q-ROFS) is a powerful tool that attracts the attention of many scholars in dealing with uncertainty and vagueness. The aim of paper is to present a new score function of q-rung orthopair fuzzy number (q-ROFN) for solving the failure problems when comparing two q-ROFNs. Then a new exponential operational law about q-ROFNs is defined, in which the bases are positive real numbers and the exponents are q-ROFNs. Meanwhile, some properties of the operational law are investigated. Later, we apply them to derive the q-rung orthopair fuzzy weighted exponential aggregation operator. Additionally, an approach for multicriteria decision-making problems under the q-rung orthopair fuzzy data is explored by applying proposed aggregation operator. Finally, an example is investigated to illustrate the feasibility and validity of the proposed approach. The salient features of the proposed method, compared to the existing q-rung orthopair fuzzy decision-making methods, are (1) it can obtain the optimal alternative without counterintuitive phenomena; (2) it has a great power in distinguishing the optimal alternative.

A three-phase method for Pythagorean fuzzy multi-attribute group decision making and application to haze management

Abstract Pythagorean fuzzy set (PFS), as a new extension of intuitionistic fuzzy set (IFS), has recently been utilized to describe uncertain information in decision making. This paper aims to develop a three-phase method for addressing multi-attribute group decision making (MAGDM) with Pythagorean fuzzy numbers (PFNs) and apply to haze management. Firstly, the normalized projection of PFNs is defined. Then the entropy and Riemann closeness degree of PFNs are proposed. Based on the normalized projection of PFNs, an extended TOPSIS method is presented to determine the DMs’ weights. A collective decision matrix is obtained by aggregating individual matrices with the DMs’ weights. Subsequently, the deviation of score from entropy and the deviation of accuracy from entropy are defined, respectively. Then a multi-objective parametric comprehensive deviation programming model is constructed to derive the attribute weights. A weighted collective matrix is obtained by the derived attribute weights. The positive ideal solution (PIS) and negative ideal solution (NIS) are determined according to the weighted collective matrix. By calculating the Riemann closeness degrees of alternatives to PIS and NIS, the ranking values of alternatives are computed to generate the ranking order of alternatives. Finally, a haze management example is elaborated to illustrate the feasibility of the proposed method. To illustrate the stability and practicality of the proposed method, the sensitivity analysis, validity test and comparative analysis are conducted.