علیرضا حمیدیه

The location problems of repair centers in the railway network have been developed to quickly and cheaply solve the failure of signaling equipment. The signaling system in railway stations guarantees the safety and smooth running of passenger and freight trains. Failure of signaling equipment is inevitable, which leads to disruption of train movement and dissatisfaction in the customer community (passengers and goods owners). Hence, it is important to quickly fix equipment failure in priority stations. The signaling equipment of each station includes interlocking, track circuit, machine needle, signal light, and axle counter. This equipment has an average monthly failure frequency, average repair time, and different thresholds for fixing the failure. This study investigates the disturbance caused by the failure of signaling equipment with the importance of stations and the average frequency of equipment failure. Importance indices were extracted to prioritize the stations. The weighting of the indicators was done using the fuzzy hierarchical analysis method. Then, the stations were prioritized based on their importance using the TOPSIS multi-criteria decision-making method. In the following, a multi-objective optimization model of location and allocation is developed to maximize the coverage of the entire maintenance network and reduce the time and cost of troubleshooting. To solve the model, the method of genetic algorithm of non-dominant sorting is used. The data of signaling equipment failure in Mashhad-Tehran railway stations is analyzed as a case study and the importance of stations and demand points is calculated. Comparing the output results with the current status of the repair centers in this study shows that the new location of the repair centers and how the stations are allocated improves the coverage number of the priority stations.

Identifying the criteria of green supplier selection is one of the most important environmental approaches in industries that play an effective role in promoting environmental performance indicators of supply chain management. In the present study, a model for predicting and selecting a green supplier is presented. The data required for this study are obtained from the records of micro and macro suppliers of an active company in the field food industry during the last ten years. In the modeling section, six well-known data mining techniques called C5.0, CART, QUEST, CHAID Decision Trees, Neural Network, and RANDOM FOREST were used. SPSS Modeler 18.0 software was implemented for data analysis. After implementing six machine learning algorithms that included four decision tree models, an artificial neural network model, and a random forest model, the best and most accurate model of all models is a multilayer artificial neural network model of Perceptron with 2 hidden layers and a total of 4 neurons were able to predict the optimal supplier with 84.63% accuracy on training data and 82% on test data. Also, due to the closeness of the accuracy of the final optimal model between the training data and test data, the presented model does not have the problem of over-fitting and predicts well.

The design of the integrated supply chain network plays a vital role in improving the operational efficiency of the company. In this regard, it is essential to integrate strategic issues such as location and transportation management with inventory management strategies in the design of the supply chain network. In the current research, the mathematical model of the three-level supply chain network, including supplier, warehouse, and retailer, has been developed by adopting two coordinated and non-coordinated inventory control strategies. The dynamic adaptive control approach is presented to deal with the uncertainty of the economic order quantity. The study's primary focus is examining the integrity or non-integrity of the inventory of supply chain echelons and its effect on the inventory allocation and the reliability levels of network storage points to guarantee a high level of service. A genetic meta-heuristic algorithm has been used to optimize the problem with an ample search space and provides reasonable solutions and reasonable improvement opportunities. The calculation results show that the amount of inventory and the costs of the entire supply chain network show a significant reduction using the coordinated inventory control strategy.

Optimizing the investment portfolio is one of the vital issues in investment management. The various fluctuations of the financial markets and the uncertainty of the parameters make using classical models a severe challenge. Therefore, the optimization of financial models in conditions of uncertainty to adapt to the real world has been the focus of researchers. In the present research, a hybrid optimization model has been developed by applying data envelopment analysis and robust programming to assess risk with uncertain inputs and outputs. The statistical population of the research was extracted from the Coin Marketcap portal, where the updated data of the adjusted price of 37 selected top cryptocurrencies was used to estimate the risk and create an optimal portfolio. A two-step approach for selecting and optimizing the stock portfolio, increasing the stability of the investment process, and comprehensive evaluation of stocks based on financial criteria is proposed. In the first stage, the performance assessment of the selected stocks is done using the robust programming-data envelopment analysis (RDEA) method. Then, in the second phase, the investment amount in eligible stocks is determined using the half-variance average and absolute standard deviation models. The performance of the proposed approach is evaluated in a case study of cryptocurrency data with increasing uncertainty. The comparative results of robust peer models with two risk measures show that the mean semi-variance model performs better in choosing and optimizing the investment portfolio.

Ever since Harry Markowitz's groundbreaking paper on the mean-variance model was published in 1952, numerous efforts have been dedicated to exploring the applications and advancements of classical models. Following the development of financial markets, active portfolio optimization has become one of the most important topics in finance. This study aimed to examine active portfolio management, a critical and delicate choice for investors, particularly concerning overall portfolio risk. The determination of an optimal stock portfolio that offers both a substantial return rate and controlled risk is consistently a subject of keen interest for analysts, investors, and even portfolio managers.
 

Many methods have been developed to measure investment risk, and the price of risky assets changes rapidly and randomly due to the complexity of the financial market. A random interval is a suitable tool for describing uncertainty with randomness and imprecision. Given the uncertainty in financial markets, this study used stochastic intervals to describe the returns of risky assets and the tail sequence risk, called the interval-valued conditional value at risk (ICVaR). The interval value in this model is an extension of the classic portfolio model, which can comprehensively reflect the complexity of the financial market and the risk-taking behavior of investors.
 

Following the findings from the real data of 10 out of 30 large corporates listed on the Tehran Stock Exchange, the ICVaR model is interpretable and compatible with the practical scenario and can be used to choose the optimal portfolio at different levels of risk and depending on the risk-taking degree of the investor. The present study used the portfolio optimization approach under a new criterion of ICVaR through the closing price, the highest price, and the lowest price on each trading day. In this model, the return range of the risky asset is taken as a random variable with an interval value. Besides, CVaR with an interval value is used to describe the risk instead of the variance at a certain level of return.
 

Uncertainties induced by asset transactions affect the predictions of investment plans. To address such challenging uncertainties in this study, a stable stochastic optimization approach was presented based on the range of optimal solutions produced by the proposed model to determine different operational options. Finally, the model developed in this study showed that investors’ subjective risk preference or aversion can be described by observing the principle of portfolio diversification, which reflects an innovation different from the classic portfolio model. Furthermore, the worst possible case was optimized in all scenarios in the model by robust optimization. The findings indicated that a narrower range corresponds to a higher level of risk aversion among investors.

The aim of this research is to investigate the effect of supply chain dynamics and flexibility in the face of disruptions on financial performance with the mediating role of supply chain resilience.

The present study is descriptive-correlational in terms of applied purpose and in terms of data collection. The statistical sample of the present study is 217 business managers of pharmaceutical companies according to the formula for determining the sample size of Cochran, observing the success ratio in this research and considering the error coefficient of 0.05. All extracted data are analyzed using SPSS23 and SmartPls statistical software in the inferential statistics section. Analysis of variance-based structural equations has been used to analyze the data.

The results of the study show that the dynamics and flexibility of the supply chain in the face of disruptions affect financial performance with the mediating role of supply chain resilience. Supply chain flexibility is also affected by the direction of supply chain disruption. However, the effects of financial performance on supply chain disruption orientation can be controlled through supply chain flexibility.

Originality/Value: 

The results of this study will be useful for designing a strategic resilience plan for pharmaceutical companies, Accordingly, it proposes to supply chain managers that when faced with disruptions, increase the dynamics and flexibility of the supply chain to improve network resilience.

The main challenge in devastating events such as the Kermanshah earthquake is the optimal location of humanitarian distribution centers, which plays an effective role in allocating relief shipments to demand centers. Therefore, balancing the complexity of the issue and the uncertainty with the constraints on aid scheduling and resource management is critical. In this regard, the location-allocation model has been developed by considering the reliability of the distribution hub set, which provides the possibility of dealing with impending disruptions after the crisis. The proposed model divides the affected area into several layers and simultaneously considers the capacity of the relief fleet. Also, a combined approach of fuzzy programming with chance constraints and robust programming has been developed to deal with parametric uncertainty.

With the thorough assessment of the disaster areas of Iran, a comprehensive model of the relief network was designed including strategic and temporary distribution hubs along with a wide range of factors and effective parameters. Subsequently, mathematical modeling was distributed by considering the reliability of the earthquake crisis distribution hub and relief according to the topography of the study area. Next, the Epsilon constraint method was applied to cover the multi-objective optimization problem and to determine non-dominant Pareto optimal solutions, and the mathematical combination of possibilistic-robust programming was used to deal with uncertainty.

The results show that the management of relief distribution and the development of strategic and operational levels of distribution based on the geographical classification of the affected area in critical conditions are effective in reducing network costs. The reliability policy used in the distribution hub set has improved the confidence capability of the humanitarian distribution network. Finally, the output results of the case study show the application and effectiveness of the extended relief network model.

Originality/Value: 

The present study, as a decision support system, facilitates relief in the regions of the country in the event of a crisis. Predicting a reliable distribution hub set with a combined transportation approach appropriate to the topography of the region ensures the optimal implementation of relief operations. Also, the developed model is operational in the areas at risk of the country.

Nowadays, companies are trying to meet customer demand due to the economic characteristics of modern global business and complex supply chain operations. Global demand uncertainty is a major challenge that is exacerbated by the occurrence of disruptions. In this study, a multi-product fuzzy stochastic nonlinear mathematical programming model is proposed to design a reliable supply chain network considering a product with modular manufacturing technology at the risk of disruption. Also, a decision support system is designed for modular production, which plays a major role in increasing the reusability of products and reducing waste. The case study, focuses on the production of cryogenic pumps used for the export of LPG, which is a critical equipment in the export of liquid propane and butane. The structure of this supply chain network includes levels of module suppliers, primary production centers, inspection centers, repair centers, reproduction centers, and customers. To deal with the disturbances, a scenario-based stochastic approach has been used and parametric uncertainty has been managed with the possibilistic-robust hybrid programming approach. The model evaluation has been performed with a precise approach in GAMS software with CPLEX solver and the sensitivity analysis has addressed the uncertain parameters. The results show that the current approach while controlling the uncertainty, ensures the optimal flow of facilities.

The purpose of this study is to explain the impact of green supply chain on economic performance, emphasizing the mediating role of green innovation, environmental management and quality management in companies listed on the Tehran Stock Exchange.

In this research, the multivariate perceptron neural network approach and modeling of variance-based structural equations in SPSS26 and SmartPls3.3.3 software have been investigated.

The results show that green supply chain management affects economic performance and increases green innovation, environmental management and quality management of economic performance. The establishment of a green supply chain has led to the observance of environmental requirements, and by observing environmental requirements, labor productivity is improved through specialized training of employees.

 In previous studies have not considered the pros and cons of the relationship between environmental management and labor productivity. In this study, according to the selection of companies listed on the Tehran Stock Exchange during the covid-19 period and the statistical sample selected by systematic elimination method and available sampling, these views were examined. Also, in the research method, most researches have only fitted the model with structural equations and regression equations, while in this research, the proposed model fits with multilayer perceptron neural network method and variance-based structural equations and finally to evaluate the performance prediction comparison model. Economically, the root mean square error index is used