جستجوی مقالات مرتبط با کلیدواژه « شبکه » در نشریات گروه « اقتصاد »

Aim and Introduction :

In the last decade, the design of the green supply chain network has become very important due to the increase in competition in global markets to achieve success, which requires the simultaneous commitment and cooperation of suppliers, manufacturers and distributors in the form of a network. One of the generalizable fields of the green supply chain network is the aquatic processing industry as one of the most important food industries that has paid less attention to optimize its supply chain in order to provide environmental benefits. In recent decades, the demand for food has increased more than the capacity to provide resources for it. Hence, the traditional food supply chain can no longer effectively manage demand. The increase in world population, globalization, global warming, drought and groundwater crisis in recent years have led to the loss of natural resources needed for animal husbandry and the reduction of marine reserves in Iran as well as in other Middle Eastern countries. The expansion of aquaculture farms has not only contributed to the development of sustainable food in the country, but has also been very effective in preserving endangered species. The aquaculture industry has grown significantly in recent decades. In this industry, green supply chain network design seems to be critical for aquatic processing.

This study is a descriptive research and applied one in terms of purpose. In this research, after examining the theoretical foundations and experimental background, the network design was proposed. The aquatic supply network was designed in three levels, for four farms, three factories, and two customers. The first level includes aquaculture farms. The second level consists of models of aquatic processing factories. The third level includes customer networks. Of course, other levels of the aquaculture supply chain, i.e. retailers, can also be considered in network design. Then the appropriate mathematical model was formulated and validated with a case study. LP-metric method was used to solve this model. The GAMS software was used in this study.

In this research, the network of green aquatic management has been designed to achieve the goals of minimizing costs and carbon dioxide emissions and maximizing the capacity of units at three levels for four farms, three factories, and two customers. In this research, after drawing the network of the problem and explaining the mathematical model, it was solved using the comprehensive criterion method with p equal to 1, 2, and 3.The value of the objective function was calculated with p equal to 0.09. By increasing the value of p, it became clear that there is no distance between the values of the objective functions and the ideal values. Then, the value of decision and binary variables was calculated. The first factory has the highest aquatic receiving capacity from the second, third and fourth farms.

In order to achieve the goals with an emphasis on reducing the carbon footprint, the first factory should process the third product, the second factory should process all three types of products, and the third factory should process the second product. In this research, certainty is considered for the parameters. If the surrounding environment is very dynamic, this turbulence can increase the uncertainty of model parameters. Therefore, it is suggested to solve the research model assuming uncertainty in the parameters in other researches. In this research, the same weight has been considered for three purposes.In this study, it is assumed that the transportation cost between nodes depends only on the distance. It is important to note that the cost of transportation between two points can be affected by various factors. Therefore, it is suggested to pay attention to these factors in other researches. Using the research model can minimize the total costs in the aquaculture network and maximize the capacity of using the components of the entire chain, bringing the environmental destruction to the lowest level. Therefore, it is suggested to use this model. One of the factors affecting the production and emission of carbon is the number of car trips. In other words, the lower the number of trips (carrying times), the lower the emissions. It is suggested managers to use a means of transportation with a higher capacity. Timely servicing of goods transport vehicles can also be effective in carbon emissions. It is recommended to periodically repair the car. In the chain under study, most of the cars were worn out and without fuel consumption reduction technology. It is suggested to replace the old vehicles carrying goods with modern and high-tech vehicles. It is recommended to use gas-fueled vehicles for transporting goods.

One of the most challenging issues in forecasting economic variables is the lack of sufficient data or  the missing data in time series. In this paper, the time series of the GDP growth rate from 1980 to 2019 for 18 Middle East and North African countries is modeled via a generalized network autoregressive model. Of the total observations, 13.42% were missed. In the proposed model, a random network is applied to the data for which nodes represent countries or related time series. Then, an autoregressive model of each node was constructed based on all the data of its multi-stage neighboring nodes. Some parameters of the model may depend on the node (local model) or can be considered the same for all network nodes (global model). The missing data are modeled by changing the weights of the network edges. Finally, the time series was predicted based on the constructed model. Because the network structure affects the model and ultimately the forecast, and on the other hand it is difficult to examine all possible networks, ten thousand un-directional random networks and 16 models including 8 local models and 8 global models on each network are considered. Out of 160,000 models, the network and the model with the least prediction error are selected as the best network and model that are used for the main prediction. The lowest in-sample predictive error was obtained at a local network, which has 64 edges and the number of corresponding model parameters is 4. Finally, the model is compared with the classical models such as AR and VAR. The results indicate the superiority of the proposed method in significantly reducing the prediction error over the AR and VAR models.

Investigating connections between financial and oil markets is important for investors and policy makers. This knowledge allows for appropriate decision making. In this paper, we measure the dynamic connections of selected stock markets in the Middle East with oil markets, gold, dollar index and euro-dollar and pound-dollar exchange rates during the period February 2007 to August 2019 in networks with different weekly horizons. In this paper, we intend to evaluate the pairwise impact of crude oil and Middle East stock markets, in particular on the Tehran Stock Exchange, and to analyze this variance using different time horizons. The results show that in all time horizons the variance of prediction error in most markets is due to the shocks within each market. The Saudi Arabian Stock Exchange has the most impact on other Middle Eastern stock markets. The dynamic connections between oil and financial decreases over time. The most important factor that impacts financial markets is conditions in neighbouring stock markets, except the Tehran Stock Exchange. Dynamic connections of the gold market with other markets is not significant. Therefore, it can be used as a tool to hedge risk.

Forecasting in financial markets has always been of intereste for researchers and investors. In particular، forecasting the market indexes is of major importance. Index forecasting methods in financial markets have been developed along with the development of time series models. In this paper a specific model for forecasting Tehran exchange price index was proposed based on mixture of experts. The results confirmed the high performance of the proposed model in modeling and forecasting Tehran exchange price index time series.