مهرداد غزنوی

By the increase in the number of production units of food and drinking industries in the country, a competitive marketing has been created between different brands producing these products. Therefore,, advertising in the field of social networks, especially in the last decade, has been one of the most common advertising solutions for various brands. The purpose of this research is to use the new fuzzy multicriteria decision-making method, called fuzzy MARCOS method, to select a suitable Instagram influencer for the advertising of drinking companies. By studying the theoretical and experimental background as well as a survey of experts in this field, twenty effective criteria on the selection of influencers for advertising were identified and ranked, and finally the seven best criteria were selected. Thereafter, the opinions of eight managers of the studied drinking company (Bismark Company) and experts familiar with the research were used to determine the weight of the criteria. Then, five selected influencers were ranked using fuzzy MARCOS multicriteria decision-making method. Using the fuzzy MARCOS multicriteria decision-making method in the field of branding and advertising in the context of social networks is one of the most important achievements of this research.

This research presents an application-oriented approach for developing machine learning models that consider the trade-off between model accuracy, processing speed, and efficient resource utilization, focusing on applications such as wearable smart systems.

A set of models is developed based on the Abstraction and Decision Fusion Architecture (ADFA), and then, using a multi-criteria decision-making approach, the appropriate models for the intended application are identified. The proposed methodology has three main phases: 1) developing models based on the ADFA, 2) defining evaluation criteria, and 3) selecting models using the Fuzzy Analytic Hierarchy Process (FAHP).

The experimental results of this research demonstrate the effectiveness of this approach in developing suitable machine learning models for applications related to wearable devices, such as smart glasses.

Originality/Value: 

This research introduces three innovations: 1) the use of ADFA for developing models for the classification of Persian handwritten characters, 2) defining a new abstraction for summarizing handwritten character images, and 3) developing a fuzzy multi-criteria decision-making approach for mapping the developed models in the ADFA to real-world applications.

Fractional programming problems is one of the efficient and common tools in many problems of real worlds, because optimizing the ratio of the purposes creates better vision than optimizing any of proposes, separately. But, as the values observed in the real world are inaccurate and ambiguous due to incomplete or inaccessible information, in this paper fuzzy numbers are used and a fuzzy linear fractional programming problem is created. In this paper, a new method for solving fully fuzzy linear fractional programming problems with trapezoidal fuzzy numbers and inequality constraints is proposed. In this method, the fuzzy problem is transformed to a multi-objective linear programming problem and then using lexicographical order the optimal solution is obtained. Finally, using some examples, the presented method is implemented practically and compared with other methods. Also, desirability of the new approach in terms of simplicity of operations and accuracy of the results is measured. p

In this paper, we deal with a linear programming problem with non-symmetric trapezoidal intuitionistic fuzzy numbers. In recent years, many authors have studied the symmetric trapezoidal intuitionistic fuzzy numbers. After defining a ranking function and arithmetic operations on these numbers, they solved the intuitionistic fuzzy linear programming problem.But the main problem with their method was that only available for symmetric trapezoidal intuitionistic fuzzy numbers. Now in order to overcome this limitation, in this paper, we present a new arithmetic and a new ordering for non-symmetric trapezoidal intuitionistic fuzzy numbers. Then, we present the general model of an intuitionistic fuzzy linear programming problems and prove a number of important theorems for solving it. Then we present the intuitionistic fuzzy simplex algorithm and finally, by presenting two examples, we will show the application of this new approach and show its superiority over the fuzzy mode.

In this paper, approximate efficient ( -efficient) solutions of multiobjective optimization problems are investigated. One of the most important methods for solving multiobjective optimization problems is to use scalarization techniques. In these methods, a single objective optimization problem corresponding to the multiobjective problem is solved, and the relationship between optimal solutions of the single objective problem and (weakly, properly) efficient solutions of the multiobjective problem is investigated. In this paper, a combination of the modified constrained and elastic constrained scalarization methods is considered, which will provide necessary and sufficient conditions for generating approximate (weakly, properly) efficient solutions. We compare the results with the necessary and sufficient conditions obtained from the modified constrained and the elastic constrained methods. The presented results can be applied for every multiobjective optimization problem without any convexity assumption for the objective functions. ‎Unlike many of the previous methods, the obtained results are also consistent with multiobjective problems with unbounded criterion space.

In this paper, a modified scalarization technique for finding Pareto optimal points of multiobjective optimization problems is provided. The proposed method is a combination of the normal constraint and elastic constraint method. First, we introduce the optimization problem of the modified method and then we present an algorithm for obtaining the set of Pareto points. Thereafter, the relationship between optimal solutions of this scalarization problem and (weakly, properly) efficient solutions of the multiobjective optimization problems are analyzed. Indeed, some necessary conditions for (weak, proper) efficiency are presented. All the provided results are established without any convexity assumption. Furthermore, we propose a new algorithm for approximating the Pareto front of multiobjective optimization problems. We solve some test problems by applying the suggested algorithm and compare the results with some existing methods, including the epsilon-constraint method, the Pascolleti-Serafini method and the NBI method. The obtained results highlight the efficiency of our approach in comparison with examined methods.