۱.
Facility location problems have often vagueness and uncertain properties. In P-center problems, this uncertainty can be in the parameters of demand nods. Firstly, in this paper, a vertex-center problem with uncertain demand nodes is considered in which the demand nodes are fuzzy and fuzzy random variables. Then, new solving methods are proposed based on possibility and necessity measures, using fuzzy and fuzzy random programming, respectively. Finally, a real case study in the city of Tabriz in Iran is presented to clarify the methods discussed in this paper. The computational results of the study indicate that these methods can be implemented for center problem with uncertain framework.
۲.
This paper reviews a two-stage stochastic programming model for integrating the decision problems of "determining capacity levels" and "determining environmental policy plans for assembly centers and consumers" for new sustainable products by considering economic and environmental factors. For this model, in the first stage, the capacity level of the new product is determined by the assembly company. Then, the amount of consumer demand is observed, which affects the decision-making of the first stage. In the second stage, the revision decision is made in line with the random scenarios that affect the decisions of the first stage. The main goal is to maximize the benefits of environmental policy programs for assembly centers and consumers by considering consumer demand under carbon policy. This paper provides a model that considers the permissible threshold limit for carbon emissions (carbon emission policy) to achieve this goal. A numerical example of the demand for reusable cars is presented. The results of this paper provide valuable insights for policymakers and assembly centers in implementing environmental policies.
۳.
One of the objectives of this research is to analyze the relationship between the quality of shared data and risk management in the supply chain. In this regard, a function for measuring visibility based on the data quality dimensions has been defined, and dimensions that are more significant in the downstream supply chain have been identified and introduced. Subsequently, a single-objective mathematical model for production planning, allocation, and pricing, considering data quality and risk was developed. Moreover, its applicability and validity were examined by solving a numerical example using GAMS software. This study highlights the importance of data quality in supply chain management, especially in the downstream supply chain where data quality significantly impacts decision- making. The results of this study can assist supply chain decision-makers in identifying the most critical dimensions of data quality and prioritizing their efforts to improve data quality. The proposed approach can also contribute to cost reduction and performance improvement by optimizing related decisions. Overall, this research contributes to the existing literature on supply chain management and data quality by providing a comprehensive framework for assessing the impact of data quality on supply chain visibility and risk management.
۴.
The functional logic of classifier models is based on the principle that, to maximize their ability to generalize—an essential factor affecting decision quality in real-world problems—it is crucial to minimize the classification error rate of available historical data. In other words, accuracy is considered the only factor affecting the generalizability of classification methods. However, due to fluctuations in financial variables, stable and reliable forecasts are also necessary for correct and profitable decision-making. Despite the importance of the reliability factor in creating stable and robust results, it has been neglected in the literature on modeling and classification. To address this research gap and enhance decision-making processes in financial applications, a modeling method based on reliability maximization is presented. This paper develops a multilayer perceptron model with the aim of maximizing reliability rather than accuracy. To evaluate the performance of the proposed model, five different financial datasets are selected from the UCI database, and its classification error rate is compared with that of the conventional multilayer perceptron model. The findings show that the reliability factor has a greater impact than the accuracy factor on the generalizability and performance of classification models. The results indicate that the proposed reliability-based multilayer perceptron model demonstrates superior efficiency and performance compared to the conventional multilayer perceptron model and can serve as a viable alternative in financial applications.
۵.
In this paper, we have modeled and optimized the multi-period stock portfolio by considering variance heterogeneity and determining the optimal number of stock packages. This model seeks to maximize the return and minimize the risk of the investment portfolio using the squared value at risk. Due to the investment portfolio in this research is based on predicted values; therefore, autoregressive modeling and variance heterogeneity have been used to predict stocks returns. Prediction is done with Python software. The linearized mathematical model for optimizing the portfolio in each period was solved using GAMS software. Furthermore, three stock portfolio designs, including predicting returns and optimizing periodic portfolio, a random portfolio, and a combination of low-risk and high-yield cases have been investigated. In two designs, the random portfolio and the portfolio with 5 high-return and 5 low-risk stocks, with the increase in the risk rate level, the annual return increases, which indicates the consistent relation between risk and return. In the periodic portfolio, this trend has been observed up to 20% risk level, while at 25% risk, there has been a decrease in return. The periodic portfolio has shown more fluctuations in profitability, while the combined approach and the random portfolio have had a more stable trend in increasing profitability with increasing risk.
۶.
The aim of the present exploratory research is the optimal simulation of a smart journalism model using a wireless sensor network. Regarding the effect of routing information transmission, in the challenge of energy consumption and the idea of smart routing (proposed), the combination of VGDRA protocol and Cuckoo meta-heuristic algorithm has been employed. Therefore, in order to enhance the efficiency of the smart journalism model, the smart mobile sink collects news information from journalists’ sensors based on the output of the two function optimizers of position and cost, through moving in a virtual dynamic route. The comparison of the simulation output in MATLAB displays compared to the VGDRA protocol, the proposed method reduces the convergence time of the network, the amount of energy consumed in the construction of the virtual backbone and reconstruction of the route, as well as increasing the lifetime of the wireless sensor network, and finally increasing the opportunity to collect news, by the journalists' sensors. The use of smart technologies in journalism is one of the solutions for competitive developments in the reconstruction of media and news agencies.
