optimization problem

Stock portfolio problems are one of the most relevant real-world problems. In this study, we discuss the portfolio's risk amount, rate of risk-return, and expected return rate under a Fermatean fuzzy environment. A linear programming problem is used to formulate a Fermatean fuzzy portfolio. The Fermatean fuzzy portfolio is converted to a deterministic form using the score function. Lingo software is used to solve these deterministic portfolio problems. The main feature of this model is that investors can select a risk coefficient to enhance predicted returns and customize their strategies according to their circumstances. An example is offered that illustrates the effectiveness and dependability of the proposed approach.

This paper has potential implications for the management of the bank. We examine a bank capital structure with contingent convertible debt to improve financial stability. This type of debt converts to equity when the bank is facing financial difficulties and a conversion trigger occurs. We use a leverage ratio, which is introduced in Basel III to trigger conversion instead of traditional capital ratios. We formulate an optimization problem for a bank to choose an asset allocation strategy to maximize the expected utility of the bank's asset value. Our study presents an application of stochastic optimal control theory to a banking portfolio choice problem. By applying a dynamic programming principle to derive the HJB equation, we define and solve the optimization problem in the power utility case.The numerical results show that the evolution of the optimal asset allocation strategy is really affected by the realization of the stochastic variables characterizing the economy. We carried out a sensitivity analysis of risk aversion, time and volatility. We also reveal that the optimal asset allocation strategy is relatively sensitive to risk aversion as well as that the allocation in CoCo and equity decreases as the investment horizon increases. Finally, sensitivity analysis highlights the importance of dynamic considerations in optimal asset allocation based on the stochastic characteristics of investment opportunities.

We consider several location inventory optimization models for the supply chain configuration of subway facilities. It includes several distribution centers and several retailers. Customer demand and redelivery time are considered random. The goal is to find the optimal locations for facilities and their inventory policy simultaneously. For this purpose, a two-phase approach based on queuing theory and stochastic optimization was developed. Today, meta-heuristic methods are often used to solve optimization problems such as facility design. in this study; The design of different units, stores, and rooms of a real large-scale subway was organized using three meta-heuristic methods Migratory Bird Optimization (MBO), Taboo Search (TS), and Simulated Simulation (SA). The results were compared with the existing subway design. As a result, the meta-heuristic methods of MBO and SA have provided the best results that improve the efficiency of the existing subway design to an acceptable level.

This paper introduces a new proposed algorithm of numerical integration evaluation regarded as optimization problem solution. The new method is characterized to have superiority features such as attractive, accurate and rapid. An improvement of polynomial regression has been done by selecting nearest neighbors points being searched around of the values of regression coefficients which calculated by using least squares method. Furthermore, Trapezoidal and Simpson methods were considered as traditional methods in numerical integration. In this regard, comparison has been done among all four methods used in simulation application via MATLAB program that have been performed to achieve the desired numerical results for the four methods. As conclusion, the proposed algorithm approved its superiority.