Fuzzy Decision-making Approach Research Articles

Analysis of risk priorities for renewable energy investment projects using a hybrid IT2 hesitant fuzzy decision-making approach with alpha cuts

The aim of this study is to identify significant risks for the renewable energy investment projects. In this regard, a novel hybrid multi-criteria decision-making (MCDM) model has been proposed based on hesitant linguistic terms and alpha level sets. This model has three different phases. Firstly, the dimensions and criteria are defined regarding the renewable energy investment risks. In the second part, the significance levels of these factors are calculated with the help of hesitant interval type-2 fuzzy (IT2F) DEMATEL-based ANP (DANP). Finally, the types of investors by the risk awareness entitled high, moderate, and low are ranked while using hesitant IT2F QUALIFLEX. In all analysis, alpha level sets are taken into consideration. The results demonstrate that organizational effectiveness and cost efficiency are the most prominent factors for renewable energy investment. On the other side, moderate risk should be taken in these projects. It is recommended that the departments within the company should be compatible with each other for the renewable energy investment projects to be more successful. For this purpose, necessary actions should be taken to improve communication between departments. Another important point is that cost control should be done effectively to contribute to the performance of the renewable energy investment projects.

Evaluation of energy alternatives for sustainable development of energy sector in India: An integrated Shannon’s entropy fuzzy multi-criteria decision approach

In this paper, conventional and renewable energy sources for sustainable development of energy sector in India are evaluated from multiple perspectives including economic, technical, social, environmental, political, and flexible criteria. An integrated Shannon’s entropy multi-criteria decision making (MCDM) method has been used for the evaluation and assessment of these sources. Thermal, gas, nuclear, solar, wind, biomass, and hydro energy options are used as the alternatives in the decision model. Shannon’s entropy method is applied to determine the weights of decision criteria, and fuzzy analytical hierarchy process (AHP) method is applied to prioritize sustainable energy alternatives. The output of the proposed model was compared with six different fuzzy MCDM techniques for the establishment of correlation index. Solar energy was shown to be particularly well suited for India followed by wind and hydro energy sources. Later, the study has developed fourteen scenarios, considering the first five sustainable energy sources (solar, wind, hydro, biomass, and gas power), to evaluate the optimal energy mix scenario for the sustainable development of energy sector in India. An optimal energy mix scenario carries the heroic development of solar, wind, and hydro energy with cross border import-export facility for the time frame of the year 2030.

Sustainability Indicator Selection by a Novel Triangular Intuitionistic Fuzzy Decision-Making Approach in Highway Construction Projects

The construction industry has been criticized as being a non-sustainable industry that requires effective tools to monitor and improve its sustainability performance. The multiplicity of indicators of the three pillars of sustainability—economic, social, and environmental—complicates construction sustainability assessments for project managers. Therefore, prioritizing and selecting appropriate sustainability indicators (SIs) is essential prior to conducting a construction sustainability assessment. The main purpose of this research is to select the most appropriate set of SIs to address all three pillars of highway sustainability by a new group decision-making approach. The proposed approach accounts for risk attitudes of experts and entropy measures under a triangular intuitionistic fuzzy (TIF) environment, to handle the inherent uncertainty and vagueness that is present throughout the evaluation process. Furthermore, new separation measures and ranking scores are introduced to distinguish the preference order of SIs. Eventually, the approach is implemented in a case study of highway construction projects and the applicability of the approach is examined. To investigate the stability and validity of computational results, a sensitivity analysis is carried out and a comparison is made between the obtained ranking outcomes and the traditional decision-making methods.

Site selection for waste vegetable oil and waste battery collection boxes: a GIS-based hybrid hesitant fuzzy decision-making approach.

The amount of municipal solid waste (MSW) has been increasing rapidly in the urban centres of developing countries during the last few decades; however, municipal solid waste management (MSWM) remains inadequate. One of the largest aspects of cost of the MSWM system is the collection of waste. This paper describes a methodology that combines geographic information systems (GIS), hesitant fuzzy linguistic term set (HFLTS), and the full multiplicative form of multi-objective optimization by ratio analysis (MULTIMOORA), to determine suitable locations for waste collection boxes (named AYPIKUT), which have been designed specifically for collection of domestic waste vegetable oil and waste batteries. It takes as case study, Atakum, a district of Samsun city, Turkey. As a solution to the problem, first, a total of 88 items have been identified for consideration by seven criteria elicited from the insights of experts, and spatial analyses were performed. Multi-criteria HFLTS was then used to determine weights of the criteria. Population density was the most significant criterion affecting the selection process, and proximity to housing complexes with more than 150 dwellings was the least important. According to the weights of the seven criteria, and three rules determined by the experts, 15 AYPIKUT locations were identified using GIS. As a final step, the alternative locations (A1-A15) were ranked with the MULTIMOORA method. A5 was the most suitable site, and A6 was the least suitable site for an AYPIKUT. The results indicated the ability of the proposed model to select the suitable locations for waste collection box.

A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost machine learning models, which suggests that heuristic methods should be tested out before directly applying machine learning models.

Collaboration Enhanced Hybrid Fuzzy Decision-Making Approach to Analyze the Renewable Energy Investment Projects

Collaboration Enhanced Hybrid Fuzzy Decision-Making Approach to Analyze the Renewable Energy Investment Projects

Dynamic economic emission dispatch of thermal-wind-solar system with constriction factor-based particle swarm optimization algorithm

PurposeDynamic economic and emission dispatch (DEED) aims to optimally set the active power generation with constraints in a power system, which should target minimum operation cost and at the same time minimize the pollution in terms of emission when the load dynamically changes hour to hour. The purpose of this study is to achieve optimal economic and emission dispatch of an electrical system with a renewable generation mix, consisting of 3-unit thermal, 2-unit wind and 2-unit solar generators for dynamic load variation in a day. An improved version of a simple, easy to understand and popular optimization algorithm particle swarm optimization (PSO) referred to as a constriction factor-based particle swarm optimization (CFBPSO) algorithm is deployed to get optimal solution as compared to PSO, modified PSO and red deer algorithm (RDA).Design/methodology/approachDifferent model with and without wind and solar power generating systems; with valve point effect is analyzed. The thermal generating system (TGs) are the major green house gaseous emission producers on earth. To take up this ecological issue in addition to economic operation cost, the wind and solar energy sources are integrated with the thermal system in a phased manner for electrical power generation and optimized for dynamic load variation. This DEED being a multi-objective optimization (MO) has contradictory objectives of fuel cost and emission. To get the finest combination of the two objectives and to get a non-dominated solution the fuzzy decision-making (FDM) method is used herein, the MO problem is solved by a single objective function, including min-max price penalty factor on emission in the total cost to treat as cost. Further, the weight factor accumulation (WFA) technique normalizes the pair of objectives into a single objective by giving each objective a weightage. The weightage is decided by the FDM approach in a systematic manner from a set of non-dominated solutions. Here, the CFBPSO algorithm is applied to lessen the total generation cost and emission of the thermal power meeting the load dynamically.FindingsThe efficacy of the contribution of stochastic wind and solar power generation with the TGs in the dropping of net fuel cost and emission in a day for dynamic load vis-à-vis the case with TGs is established.Research limitations/implicationsCost and emission are conflicting objectives and can be handled carefully by weight factors and penalty factors to find out the best solution.Practical implicationsThe proposed methodology and its strategy are very useful for thermal power plants incorporating diverse sources of generations. As the execution time is very less, practical implementation can be possible.Social implicationsAs the cheaper generation schedule is obtained with respect to time, cost and emission are minimized, a huge revenue can be saved over the passage of time, and therefore it has a societal impact.Originality/valueIn this work, the WFA with the FDM method is used to facilitate CFBPSO to decipher this DEED multi-objective problem. The results reveal the competence of the projected proposal to satisfy the dynamic load demand and to diminish the combined cost in contrast to the PSO algorithm, modified PSO algorithm and a newly developed meta-heuristic algorithm RDA in a similar system.

An optimal fuzzy decision-making approach for laser powder bed fusion of AlSi10Mg alloy

Abstract The present work proposes a fuzzy decision-making approach for the optimisation of the Laser Powder Bed Fusion (L-PBF) processing of AlSi10Mg alloy. First, a systematic approach based on Design of Experiment was adopted in order to identify and explain the effect of each operational parameter, i.e. volumetric energy density and building orientation, on the response variables, i.e. ultimate tensile strength, hardness and roughness. Then, a combined fuzzy-multi-objective genetic algorithm model was developed and tested in order to find the best input parameters’ combination able to satisfy the requirement of the highest mechanical performances. The use of a multi-objective optimisation based on genetic algorithm concerned the computation of optimal fuzzy numbers in order to take into account a precise percentage of experimental data for a given degree of membership, in combination with the smallest uncertainty level. Moreover, from the technological point of view, the proposed experimental results showed a similar and even improved mechanical properties of the alloy, if compared with the die casted one, except for the roughness. Finally, the fuzzy process maps generated allow to select the operational parameters in order to obtain the desired process output, in combination with the lowest uncertainty level, providing, as additional information, how much the accuracy of the model and the process varies by changing those operational parameters.

Unbalance mitigation by optimal placement of static transfer switches in low voltage distribution feeders

With the rapid proliferation of residential rooftop photovoltaic (PV) systems, current and voltage unbalance issues have become a matter of great concern in low voltage (LV) distribution feeders. To overcome the issues, this study proposes a model to optimally rephase customers and PVs among the three phases via static transfer switches (STSs). The optimal STS placement is also considered in the model to achieve a cost effective solution with optimum number and location of STSs. The objective is to minimise total energy losses caused by current unbalance, minimise the number of STSs, and keep voltage unbalance along the feeder within the acceptable range. The model is solved via a non-dominated sorting genetic algorithm-II (NSGA-II) which provides a Pareto front. A fuzzy decision-making approach is then applied to choose the final solution among the Pareto front points. The proposed model is simulated on the IEEE 123-Node Test Feeder. The simulations are conducted in MATLAB where the COM interface capability is used to call OpenDSS to evaluate NSGA-II populations. According to the achieved results, the proposed model can effectively and affordably apply STSs to mitigate unbalance issues in LV feeders hosting high penetration of rooftop PVs.

Analysing the voice of customers by a hybrid fuzzy decision-making approach in a developing country's automotive market

PurposeThis paper analyses the voice of customers (VoCs) using a hybrid clustering multi-criteria decision-making (MCDM) approach. The proposed method serves as an efficient tool for how to approach multiple decision-making involving a large set of countrywide customer complaints in the Iranian automotive sector.Design/methodology/approachThe countrywide data comprising 3,342 customer complaints (VoCs) were gathered. A total of seven determinant complaint criteria were identified in brainstorming sessions with three groups (six each) of experts employing the fuzzy Delphi method. The weights of these criteria were assigned by applying the fuzzy best–worst method (FBWM) to identify the severity of the complaints. Subsequently, the complaints were clustered into five categories with respective customer locations (province), car type and manufacturer using the K-mean method and further prioritised and ranked employing the fuzzy complex proportional assessment of alternatives (FCOPRAS) method.FindingsThe results indicated that the majority of complaints (1,027) from the various regions of the country belonged to one specific model of car made by a particular producer. The analyses revealed that only a few complaints were related to product quality, with the majority related to service and financial processes including delays in automobile delivery, delays in calculating monthly instalments, price variation, failure to provide a registration ( licence) and failure to supply the agreed product. The proposed method is an efficient way to solve large-scale multidimensional problems and provide a robust and reliable set of results.Practical implicationsThe proposed method makes it much easier for management to deal with complaints by significantly reducing their number. The highest-ranked complaints from customers of the car industry in Iran are those related to delivery time, price alternations, customer service support and quality issues. Surveying the list of complaints shows that paying attention to the four most voiced complaints can reduce them more than 54%. Management can make appropriate strategies to improve the production quality as well as business processes, thus producing a significant number of customer complaints.Originality/valueThis paper proposes a comprehensive approach to critically analyse the VoCs by combining qualitative and decision-making approaches including K-mean, FCOPRAS, fuzzy Delphi and FBWM. This is the first paper that analyses the VoCs in the automotive sector in a developing country’s context involving large-scale decision-making problem-solving.

Critical Factors Affecting Construction Price Index: An Integrated Fuzzy Logic and Analytical Hierarchy Process

Nowadays, many construction engineering and technology enterprises are evolving to find that prosperity is driven and inspired by an open economy with dynamic markets and fierce multifaceted competition. Besides brand and product uniqueness, the ability to quickly provide customers with quotes are matters of concern. Such a requirement for prompt cost estimation of construction investment projects with the use of a construction price index poses a significant challenge to contractors. This is because the nature of the construction industry is shaped by changes in domestic and foreign economic factors, socio-financial issues, and is under the influence of various micro and macro factors. This paper presents a fuzzy decision-making approach for calculating critical factors that affect the construction price index. A qualitative approach was implemented based on in-depth interviews of experts in the construction industry in Vietnam. A synthetic comparison matrix was calculated using Buckley approach. The CoA approach was applied to defuzzified the fuzzy weights of factors that affect the construction price index. The research results show that the top five critical factors affecting the construction price index in Vietnam are (1) consumer price index, (2) gross domestic product, (3) basic interest rate, (4) foreign exchange rate, and (5) total export and import.

Kano-based mapping of innovation strategies for renewable energy alternatives using hybrid interval type-2 fuzzy decision-making approach

In this study, it is aimed to identify innovative strategies for different renewable energy alternatives. In this context, the criteria that affect the effectiveness of renewable energy investments are first analyzed with the IT2 fuzzy DEMATEL. Then, 16 different strategies based on the Kano model are created. Also, appropriate innovative strategies have been determined for 5 different renewable energy types with IT2 fuzzy TOPSIS. Additionally, these alternatives are also ranked by using IT2 fuzzy VIKOR to make a comparative evaluation. Moreover, Monte Carlo simulation technique has been implemented to check and understand the objectiveness of the evaluation results. It is concluded that all results are quite coherent. The findings indicate that for the technical requirement dimension, the most important criteria are the availability of equipment and technological infrastructure. Regarding the customer satisfaction, it is identified that the possibility of sustainable consumption and competitive price play a key role. It is also determined that wind and solar energy alternatives are quite appropriate for all kinds of market conditions to create innovative strategies. For the biomass energy investments, new products should be developed by making radical innovations. In addition, it is seen that the hydroelectric energy alternative is not very suitable for its current form due to the low efficiency. Therefore, a detailed financial analysis should be carried out to solve this problem. Finally, as for geothermal energy investments, technical requirements should be satisfied to make more effective investments.

A probabilistic multi-objective approach for FACTS devices allocation with different levels of wind penetration under uncertainties and load correlation

This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the Multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30-bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.

Stochastic renewable energy resources integrated multi-objective optimal power flow

The modern state of electrical system consists the conventional generating units along with the sources of renewable energy. The proposed article recommends a method for the solution of single and multi-objective optimal power flow, integrating wind and solar output energy with traditional coal-based generating stations. In the first part of the article, the two wind power plants and one solar PV power plants are incorporated with the thermal power plants. The optimal power flow problem of single and conflicting multi-objectives are taken with this scenario. The second part of the paper, solar power plant is replaced with another wind power plant with the conventional coal-based power plants. The techno-economic analysis are done with this state of electrical system. In proposed work, lognormal and weibull probability distribution functions are also utilized for predicting solar and wind outputs, respectively. A non-dominated multi-objective moth flame optimization technique is used for the optimization issue. The fuzzy decision-making approach is applied for extracting the best compromise solution. The results are validated though adapted IEEE-30 bus test system, which is incorporated with wind and solar generating plants.

Fuzzy decision-making in laser-assisted joining of polymer-metal hybrid structures

A fuzzy decision-making approach was developed to optimize laser-assisted joining process of polymer-metal hybrid structures. A systematic approach based on Design of Experiment was adopted to identify and explain the effect of each process parameter, i.e. laser energy and laser power, on the response variables, i.e. maximum operating temperature and shear strength. Then, a combined fuzzy-genetic algorithm model was developed to find the best input parameters’ combination capable to satisfy the requirement of the best processing performances, in terms of appropriate range of operating temperature and highest shear strength. The use of genetic algorithms involved the choice of the best nominal regression model and the optimisation of the fuzzy numbers. This enabled to account most of the experimental data in combination with the smallest uncertainty level. Experimental results showed the success of the joining process ensuring sufficient adhesion of the hybrid structure over time, with shear strength of almost 29 MPa by using a laser power of 150 W and energy of 2 kJ. Moreover, the fuzzy process maps enable the selection of the process parameters with the aim of achieving desired process output along with the lowest uncertainty level. In addition, it also provides the amount of uncertainty of the model. The results indicated that a low laser energy level of 2250 J and a laser power in the range 120–140 W would provide the optimal solution in terms of operating temperature (i.e. between 343 and 520 °C), shear strength (i.e. greater than 26 MPa) and uncertainty level (i.e. membership level greater than 0.9).

Fuzzy-Based Relaying Scheme for Transmission Line Based on Unsynchronized Voltage Measurement

ABSTRACT This study aims to develop a new relaying scheme for the complete protection of transmission line based on only voltage data. Fault detection and classification is obtained from the fundamental component of the voltage signal collected from one end of the line. Magnitudes of positive sequence component of voltage signals from both ends of the line have been considered for fault location. As only magnitude of voltage signal is used, the requirement for data synchronization can be avoided, and the error caused by current transformers (CT) is eliminated. Moreover, positive sequence components exist for all types of faults, unlike negative sequence components. Hence, all types of faults can be located. Fuzzy decision approach has been proposed to avoid computationally expensive training of expert systems. The proposed scheme has been tested for different dynamic situations in MATLAB/Simulink software. The superiority of the scheme has been established by considering coupling capacitor voltage transformer (CCVT) transient error, weak infeed condition, change of transmission voltage level and line loading.

A Hybrid Fuzzy Decision-Making Approach to Select the Best online-taxis business

In the recent decade, significant growth of internet-based platforms and changes in people’s moving preferences has led to an increase in the electronic taxis businesses. Hence, investigating the factors affected by such businesses can help increase their profits and, at the same, time their customers’ satisfaction level. In this study, a hybrid fuzzy decision-making approach is proposed to examine the best online-taxis business selection problem. The proposed framework firstly determines the interrelationships between criteria and sub-criteria, by applying the Fuzzy Decision making trial and evaluation laboratory (FDEMATEL) method. Then, the weights of the criteria and sub-criteria are calculated using an integrated Fuzzy Best-Worst Method (FBWM) and the Fuzzy Analytic network process (FANP). In this regard, at first, the local weights of indicators are calculated using the FBWM regardless of interrelationships between them. Then, the final (i.e. global) weights of indicators, considering their interrelationships, are measured employing the FANP method. Afterwards, the feasible alternatives are prioritized by employing the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) method. For each step of the proposed framework, a questionnaire is designed and distributed between experts. The results show that the most important criteria and sub-criteria for passengers are cost and reasonable price, respectively. Finally, some managerial insights are provided.

Analysis of Innovation Performance for Retail Banking Industry With the Hybrid Fuzzy Decision-Making Approach

This study aims to evaluate the innovation performance of the Turkish banking industry. For this purpose, eight different financial and nonfinancial criteria are identified as a result of literature review. Moreover, five biggest Turkish deposit banks are selected as alternatives. Interval type 2 fuzzy decision-making trial and evaluation laboratory (IT2 FDEMATEL) is taken into account to weight the dimensions. On the other side, to rank the alternatives, interval type 2 fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (IT2 FVIKOR) approach is considered. The findings show that market share and return on investment are the most important factors in the innovation performance of retail banking services. This situation gives information that the when banks are more successful in the market, they can make investment to the innovation more effectively. The main reason is that these banks have enough capacity to make this kind of investments. Thus, it is recommended that Turkish banks should first make an efficiency analysis to increase their profit margin so that their capacities can be improved. For this purpose, many different factors should be taken into account by Turkish banks, such as personnel competency and technological development. Within this scope, these banks should make financial analysis of the innovation effectively by generating a qualified team. However, necessary research and development activities should be conducted to reach this objective. Hence, it can be possible to minimize ineffective innovation for the banks.

Decision to Adopt Neuromarketing Techniques for Sustainable Product Marketing: A Fuzzy Decision-Making Approach

Sustainable products and their marketing have played a crucial role in developing more sustainable consumption patterns and solutions for socio-ecological problems. They have been demonstrated to significantly decrease social consumption problems. Neuromarketing has recently gained considerable popularity and helped companies generate deeper insights into consumer behavior. It has provided new ways of conceptualizing consumer behavior and decision making. Thus, this research aims to investigate the factors influencing managers’ decisions to adopt neuromarketing techniques in sustainable product marketing using the fuzzy analytic hierarchy process (AHP) approach. Symmetric triangular fuzzy numbers were used to indicate the relative strength of the elements in the hierarchy. Data were collected from the marketing managers of several companies who have experience with sustainable product marketing through online shopping platforms. The results revealed that the accuracy and bias of neuromarketing techniques have been the main critical factors for managers to select neuromarketing in their business for advertising and branding purposes. This research provides important results on the use of neuromarketing techniques for sustainable product marketing, as well as their limitations and implications, and it also presents useful information on the factors impacting business managers’ decision making in adopting neuroscience techniques for sustainable product development and marketing.

Multi-Criteria Pythagorean Fuzzy Group Decision Approach Based on Social Network Analysis

With the development of social media, the social relationships among group decision makers should be taken into consideration when addressing group decision making problems. As an extension of the fuzzy set, the Pythagorean fuzzy set has recently been applied to depict the uncertainty in practical group decision problems. The purpose of this paper is to propose a multi-criteria Pythagorean fuzzy group decision approach considering social relations. In this paper, the similarity degree and the paths between any two experts in a social network are combined to construct a connection strength matrix to detect the leader among all the experts. A leader-following consensus reaching algorithm is proposed to adjust the multi-criteria Pythagorean fuzzy decision making matrix. Moreover, a procedure for the multi-criteria Pythagorean fuzzy group decision approach based on social network analysis is proposed. We use an example to illustrate the feasibility and advantages of the proposed method.