Sapan Kumar Das

The identification of optimal stock or portfolio options is a critical concern for investors aiming to maximize profitability within financial markets. With the increasing complexity of available alternatives and the growing volume of financial data, selecting the most suitable investment has become more challenging. Decision-makers often face difficulties in navigating these vast data sets and require robust support tools to simplify and enhance the decision-making process. This study proposes a three-phase approach designed to reduce data complexity and facilitate more detailed analysis. In the initial phase, firms demonstrating low operational efficiency, as indicated by their inventory turnover ratio, were excluded from further consideration. In the subsequent phase, data envelopment analysis (DEA) was employed to assess the efficiency of remaining firms, with those exhibiting efficiency scores lower than one being removed from further investigation. Finally, the third phase involved determining the relative importance of each financial ratio through the calculation of their respective weights, allowing for the ranking of firms based on these adjusted values. The results of this approach provide decision-makers with a refined list of viable investment options, contributing to more informed stock portfolio optimization decisions.

The prediction of a real-life problem like in industrial sector or health sector the outcome is impossible or sometimes it is difficult. Due to high information uncertainty and complicated influencing factors of industrial sector, the traditional data-driven prediction approaches can hardly reflect the real changes in practical situation. Fuzzy programming is a powerful prediction reasoning and risk assessment model for uncertain environment. This article mainly explores and applies a modified form of fuzzy programming; namely Fuzzy Linear Fractional Programming Problem (FLFPP) having the coefficients of the objectives and constraints as triangular fuzzy numbers (TFNs). The FLFPP is converted into an equivalent crisp multi-objective linear fractional programming problem (MOLFPP) and solved individually to associate an aspiration level to it. Then by applying fuzzy goal programming (FGP) technique the maximum degree of each membership goal is obtained by minimizing the negative deviational variables. We carry out two industrial application simulations in a hypothetical industrial scenario. Our study shows that the proposed model is practical and applicable to the uncertain practical environment to realize the prediction and the obtained results are compared with that of the existing methods.

Purpose: In this paper, a combined TODIM-BSC method with the neutrosophical approach for evaluating the performance of a private insurance company has been proposed. In other words, first, using the BSC technique, and identify the performance evaluation indicators, then evaluate the performance of the insurance company's agencies and rank them with the TODIM decision-making method.Design/methodology/approach: The insurance industry has a special prestige and importance in domestic and foreign trade. The evaluation of insurance companies, in addition to informing the stakeholders, increases competition, industry dynamism, sustainable and balanced development of society. The purpose of this paper is to establish a model for evaluating the performance of private insurance companies by adopting multiple-attribute decision-making and Balanced Scorecard (BSC) with single-values neutrosophic numbers (SVNNs) which will be applied by considering a set of indicators and alternatives deliberated with different viewpoints.Findings: A case study of the private insurance agencies in one of the provinces of Iran based on 26 criteria of agencies is used to confirm the practicality and effectiveness of the proposed model. Finally, there was a discussion about why the results are logical, which shows the strength and robustness of the proposed framework.Originality/value: To the best of our knowledge, no study has been performed to evaluate the performance of a real-world problem with the integrated TODIM-BSC method in a neutrophilic environment. Therefore, this paper can be effective in bridging the existing research gap and expanding our knowledge of the discussion of evaluating the performance of organizations or companies. Besides, by using these results, the authors can help the planners of these companies as well as similar organizations in attracting satisfaction and retaining target customers.

In this paper, Linear Fractional Programming (LFP) problems have been extended to neutrosophic sets (NSs) and the operations and functionality of these laws are studied. Moreover, the new algorithm is based on aggregation ranking function and arithmetic operations of triangular neutrosophic sets (TNSs). Furthermore, for the first time, in this paper, we take up a problem where the constraints are both equality and inequality neutrosophic triangular fuzzy number. Lead from genuine issue, a few numerical models are considered to survey the legitimacy, profitability and materialness of our technique. At last, some numerical trials alongside one contextual analysis are given to show the novel techniques are better than the current strategies.

In this article, we address a fully fuzzy triangular linear fractional programming (FFLFP) problem under the condition that all the parameters and decision variables are characterized by triangular fuzzy numbers. Utilizing the computation of triangular fuzzy numbers and Lexicographic order (LO), the FFLFP problem is changed over to a multi-objective function. Consequently, the problem is changed into a multi-objective crisp problem. This paper outfits another idea for diminishing the computational complexity, in any case without losing its viability crisp LFP issues. Lead from real-life problems, a couple of mathematical models are considered to survey the legitimacy, usefulness and applicability of our method. Finally, some mathematical analysis along with one case study is given to show the novel strategies are superior to the current techniques.

Purpose: We formulate a linear fractional programming (LFP) problem in which costs of the objective functions and constraints all are taken to be triangular fuzzy numbers. Methodology: The fuzzy LFP problem is transformed into an equivalent crisp line fractional programming (CLFP) problem by using the centroid ranking function. This proposed method is based on crisp LFP and has a simple structure. Findings: To show the efficiency of our proposed method a real life problem has been illustrated. The discussion of the practical problem will help decision makers to realise the usefulness of the CLFP problem. Value: Using centroid ranking function, we overcome the all limitations of our day to day real life problem. Finally, a result analysis is also established for applicability of our method.

Real humankind problems have different sorts of ambiguity in the creation, and amidst them, one of the significant issues in solving the integer linear programming issues. In this commitment, the conception of aggregation of ranking function has been focused on a distinct framework of reference. Here, we build up another framework for neutrosophic integer programming issues having triangular neutrosophic numbers by using the aggregate ranking function. To legitimize the proposed technique, scarcely numerical analyses are given to show the viability of the new model. At long last, conclusions are talked about.