Current Issue : April - June Volume : 2019 Issue Number : 2 Articles : 5 Articles
In complex decision making, using multicriteria decision-making (MCDM)methodologies is the most scientific way to ensure an\ninformed and justified decision between several alternatives.MCDMs have been used in different ways andwith several applications\nthat proved their efficiency in achieving this goal. In this research, the advantages and disadvantages of the different MCDM\nmethodologies are studied, along with the different techniques implemented to increase their accuracy and precision.Themain aim\nof the study is to develop a hybridMCDMprocess that combines the strengths of severalMCDMmethods and apply it to choose the\nbest fit maintenance policy/strategy for industrial application.Moreover, fuzzy linguistic terms are utilized in all of the usedMCDM\ntechniques in order to eliminate the uncertainty and ambiguity of the results. Through an extensive literature review performed\non studies that have used MCDMmethods in a hybrid context and using fuzzy linguistic terms, a model is developed to use fuzzy\nDEMATEL-AHP-TOPSIS hybrid technique. The model with its application is the first of its kind, which combines the strengths of\nfuzzy DEMATEL in establishing interrelationships between several criteria, as well as performing a pairwise comparison between\nthe criteria for prioritization using the fuzzy AHP method. Thereafter, the alternatives are compared using fuzzy TOPSIS method\nby establishing negative and positive solutions and calculating the relative closeness for each of the alternatives. Furthermore, six\nmain criteria, twenty criteria, and five alternatives are selected from the literature for the model application....
The concept of Pythagorean fuzzy sets (PFSs) was initially developed by Yager in 2013, which provides a novel way to model\nuncertainty and vaguenesswith high precision and accuracy compared to intuitionistic fuzzy sets (IFSs).The conceptwas concretely\ndesigned to represent uncertainty and vagueness in mathematical way and to furnish a formalized tool for tackling imprecision to\nreal problems. In the present paper, we have used both probabilistic and nonprobabilistic types to calculate fuzzy entropy of PFSs.\nFirstly, a probabilistic-type entropy measure for PFSs is proposed and then axiomatic definitions and properties are established.\nSecondly, we utilize a nonprobabilistic-type with distances to construct newentropymeasures for PFSs. Then a minâ??max operation\nto calculate entropy measures for PFSs is suggested. Some examples are also used to demonstrate suitability and reliability of the\nproposed methods, especially for choosing the best one/ones in structured linguistic variables. Furthermore, a newmethod based on\nthe chosen entropies is presented for Pythagorean fuzzy multicriterion decision making to compute criteria weights with ranking\nof alternatives. A comparison analysis with the most recent and relevant Pythagorean fuzzy entropy is conducted to reveal the\nadvantages of our developed methods. Finally, this method is applied for ranking China-Pakistan Economic Corridor (CPEC)\nprojects.These examples with applications demonstrate practical effectiveness of the proposed entropy measures....
The main purpose of this paper is to investigate the application potential of ordered fuzzy numbers (OFN) to support evaluation of\nnegotiation offers.The Simple AdditiveWeighting (SAW) and the Technique for Order of Preference by Similarity to Ideal Solution\n(TOPSIS) methods are extended to the case when linguistic evaluations are represented byOFN.We study the applicability ofOFN\nfor linguistic evaluation negotiation options and also provide the theoretical foundations of SAW and TOPSIS for constructing a\nscoring function for negotiation offers. We show that the proposed framework allows us to represent the negotiation information\nin a more direct and adequate way, especially in ill-structured negotiation problems, allows for holistic evaluation of negotiation\noffers, and produces consistent rankings, even though new packages are added or removed. An example is presented in order to\ndemonstrate the usefulness of presented fuzzy numerical approach in evaluation of negotiation offers....
The technology of power electronic systems has diversified into industrial, commercial, and residential areas. Developing a strategy\nto improve the performance of the electrical energy of an electric vehicle (EV) requires an analysis of the model that describes it.\nEVs are complex mechatronic systems described by nonlinearmodels and, therefore, its study is not an easy task. It can improve the\nperformance of a battery bank by creating newbatteries that allowfor greater storage or by developing a management energy system.\nThis article shows the development of a power supply management system based on fuzzy logic for an electric vehicle, in order to\nminimize the total energy consumption and optimize the battery bank. The experimental result is shown using the fuzzy controller\nunder standard operating conditions.An increase in battery performance and overall performance of energy consumption is shown.\nSpeed signals acquired show improvements in some dynamic, such as overshoot, settling time, and steady-state error parameters.\nIt is shown that this fuzzy controller increases the overall energy efficiency of the vehicle...
Fuzzy regression analysis is an important regression analysis method to predict\nuncertain information in the real world. In this paper, the input data are\ncrisp with randomness; the output data are trapezoid fuzzy number, and\nthree different risk preferences and chaos optimization algorithm are introduced\nto establish fuzzy regression model. On the basis of the principle of the\nminimum total spread between the observed and the estimated values,\nrisk-neutral, risk-averse, and risk-seeking fuzzy regression model are developed\nto obtain the parameters of fuzzy linear regression model. Chaos optimization\nalgorithm is used to determine the digital characteristic of random\nvariables. The mean absolute percentage error and variance of errors are\nadopted to compare the modeling results. A stock rating case is used to evaluate\nthe fuzzy regression models. The comparisons with five existing methods\nshow that our proposed method has satisfactory performance....
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