Evaluating the Main Battle Tank Using Fuzzy Number Arithmetic Operations

Since the descriptions and judgements on weapon systems are usually linguistic and fuzzy,it is more realistic to evaluate weapon systems in the framework of fuzzy sets theory. In thispaper, a new method to evaluate the best main battle tank is proposed. It can be seen that theproposed method is more efficient due to the fact that, by canonical representation of arithmeticoperation on fuzzy numbers, simple arithmetic operations on crisp numbers are used, instead ofcomplicated fuzzy numbers operations. In addition, the final scores of each alternative can berepresented as crisp numbers. As a result, the order of alternatives can be determined withoutthe procedure of ranking fuzzy numbers. Finally, a numerical example to evaluate the best mainbattle tanks is used to illustrate the efficiency of the proposed method

FUZZY DELAY DIFFERENTIAL EQUATIONS WITH HYBRID SECOND AND THIRD ORDERS RUNGE-KUTTA METHOD

This paper considers fuzzy delay differential equations with known statedelays. A dynamic problem is formulated by time-delay differential equations and an efficient scheme using a hybrid second and third orders Runge-Kutta method is developed and applied. Runge-Kutta is well-established methods and can be easily modified to overcome the discontinuities, which occur in delay differential equations. Our objective is to develop a scheme for solving fuzzy delay differential equations. A numerical example was run, and the solutions were validated with the exact solution. The numerical results from C program will show that the hybrid Runge-Kutta scheme able to calculate the fuzzy solutions successfully

Hybrid runge-kutta method for solving linear fuzzy delay differential equations with unknown state-delays

In this research, a new method to solve the Fuzzy Delay Differential Equations (FDDEs) with unknown state-delays constrained optimization problem is introduced. This method is based on the coupling of second and third orders Runge- Kutta (RK) method called hybrid RK method. The main goal of this thesis is to identify the unknown state-delays using experimental data. RK methods are chosen because they are well-established and can be easily modified to overcome the discontinuities which occur in Delay Differential Equations (DDEs) especially outside uniform nodes with delay step-size. Numerical results of FDDEs from the hybrid RK methods are compared with exact solutions derived from stepwise approach using Maple software. The relative errors are calculated for the purpose of accuracy checking on these numerical schemes. In this study, a dynamic optimization problem in which the state-delays are decision variables is also imposed; with its formulated cost function. The gradient of the cost function is computed by solving auxiliary FDDEs. By exploiting the results, the state-delay identification problem can be solved efficiently and accurately using a gradient-based optimization method. In addition, a C program has been developed based on hybrid RK methods for solving these problems. Consequently, the results show that the new hybrid scheme is an efficient numerical technique in solving all the problems above with acceptable errors

A Fuzzy Modelling of a Hybrid MCDM Method for Supplier Selection = Egy hibrid MCDM-módszer fuzzy modellezése a beszállítók kiválasztásához

This article examines the significance of supplier selection in the procurement process, which has grown in prominence as a result of globalization and outsourcing. When selecting the best suppliers, supply chain managers must consider a variety of quantitative and qualitative aspects, as these have a substantial impact on supply chain performance. Multi-criteria decision-making (MCDM) approaches can help in this process by taking into account many competing considerations. However, due to uncertainties and ambiguity, supplier selection is a complex process, and fuzzy multi-criteria decision-making approaches can be used to determine the best supplier for the company's key operations. This research suggests a hybrid MCDM strategy that makes use of fuzzy modeling to help with complex decision-making processes. Organizations can improve their supply chain performance by selecting the best supplier based on numerous parameters such as cost, quality, delivery time, and supplier reputation. Jelen tanulmány a beszállítók kiválasztásának jelentőségét vizsgálja a beszerzési folyamatban, amely a globalizáció és a kiszervezés következtében egyre nagyobb jelentőségre tett szert. A legjobb beszállítók kiválasztásakor az ellátási lánc vezetőinek számos mennyiségi és minőségi szempontot kell figyelembe venniük, mivel ezek jelentős hatással vannak az ellátási lánc teljesítményére. A többkritériumos döntéshozatal (MCDM) megközelítések számos egymással versengő szempont figyelembevételével segíthetnek ebben a folyamatban. A bizonytalanságok és a többértelműség miatt azonban a beszállító kiválasztása összetett folyamat, és a fuzzy többkritériumú döntéshozatali megközelítések segítségével meghatározható a vállalat kulcsfontosságú műveleteihez legmegfelelőbb beszállító. Ez a kutatás egy hibrid MCDM stratégiát javasol, amely a fuzzy modellezést használja fel a komplex döntéshozatali folyamatok segítésére. A szervezetek számos paraméter, például a költségek, a minőség, a szállítási idő és a beszállító hírneve alapján a legjobb beszállító kiválasztásával javíthatják ellátási láncuk teljesítményét

Sustainable and Renewable Energy Power Plants Evaluation by Fuzzy TODIM Technique

Sustainable and renewable energy systems are an effective solution to depletion of fossil energy resources and prevent serious environmental problems resulted from energy production. Turkey has rich renewable energy potential due to its geographical features. In this regard, the government puts emphasis on increasing renewable energy utilization rate in meeting energy demand of the country. Therefore encouragement policies are implemented in this field and energy investors are supported economically. In his study, we aimed to find out the best performing sustainable and renewable energy alternative and thus to guide decision makers on energy investments. Therefore we evaluated four energy power plant types, which are solar, wind, hydroelectric and landfilled gas (LFG). For the evaluation of the alternatives, there are many factors to consider and multicriteria decision making (MCDM) methods are an appropriate approach for this issue. In this regard, we determined 22 evaluation criteria in technical, economical and environmental aspect and applied TODIM technique. It is based on prospect theory and the most significant difference from the other MCDM methods is to deal with risk in decision-making. In order to cope with vagueness and uncertainty in this evaluation process, we integrated fuzzy sets into the system. Finally we evaluated the results obtained and presented a sensitivity analysis at the end

Wind Energy Development Site Selection Using an Integrated Fuzzy ANP-TOPSIS Decision Model

The identification of appropriate locations for wind energy development is a complex problem that involves several factors, ranging from technical to socio-economic and environmental aspects. Wind energy site selection is generally associated with high degrees of uncertainty due to the long planning, design, construction, and operational timescales. Thus, there is a crucial need to develop efficient methods that are capable of capturing uncertainties in subjective assessments provided by different stakeholders with diverse views. This paper proposes a novel multi-criteria decision model integrating the fuzzy analytic network process (FANP) and the fuzzy technique for order performance by similarity to ideal solution (TOPSIS) to evaluate and prioritize the potential sites for wind power development. Four major criteria, namely economic, social, technical, and geographical, with nine sub-criteria are identified based on consultation with wind farm investors, regulatory bodies, landowners and residents, developers and operators, component suppliers, ecologists, and GIS analysts. The stakeholders’ preferences regarding the relative importance of criteria are measured using a logarithmic least squares method, and then the alternative sites are prioritized based on their relative closeness to the positive ideal solution. The proposed model is applied to determine the most appropriate site for constructing an onshore wind power plant consisting of 10 wind turbines of 2.5 MW. Finally, the results are discussed and compared with those obtained using the traditional AHP, ANP and ANP-TOPSIS decision-making approaches