فهرست مطالب seyed farid ghaderi

Human resource management is one of the most important priorities in the aviation industry to enhance flightsafety. In the meantime, pilots resources management is an important factor to reduce cockpit crew’s errors as well as increasing flight safety and quality. In order to monitor all threats and errors (as managed or not managed by pilots), considering the criteria which have a direct impact on the pilots' resources management, is the basic and important issues to maintain an airline at an acceptable level of safety and quality performance. There is a gap in literature review to identify and prioritize the effective flight safety criteria which have a direct relationship with the pilots' errors. In this paper, a method for pilot’s resources management is provided to identify criteria and sub-criteria as well as prioritizing them in order to examine the degree of correlation and its effect on flight crew error. The statistical population of this research is active airline pilots in Airbus fleets of wide-body and narrowbody aircrafts (A total of 150 pilots were selected from 200 flights). Simple random sampling has been used to collect the data which are needed for the research as well as their analysis. In this research, the data-oriented method (Exploratory Data Analysis, regression, Analysis of Variance) and content validity method are applied in order to identify the criteria affecting the rate of pilot’s error and also prioritizing the criteria. Discovery of various effective criteria (communication, awareness of the environment, effective decision making and problem solving approach, leadership, stress management) on the pilots' resources management and also the extent of its relationship with pilots' errors and how to evaluate it, including the findings of this research.

This paper presents an empirical investigation to study the effect of interest rate, inflation and gold price on stock price by considering the historical monthly prices from October, 2021, to October, 2022. The study also uses the prices of Exxon Mobil Corporation as the biggest oil producer in the United States for the same period. The study uses monthly 10-year Treasury-Yield, inflation and gold price for the same period. The study uses multiple linear regression and finds that two independent variables of inflation and interest rates influence oil price positively while gold price has no meaningful effect on oil price. In our survey, Interest rate is the most determinant of the price of Exxon Mobil share price (β = 11.66, t= 9.22) followed by Inflation (β = 6.506, t= 15.69) when the level of significance is one percent. The survey also investigates OPEC+ as well as the United States government actions during the study and finds minor influence on share price. The results indicate that any short-term interruption of oil price may lead to short to medium term on share price increase while the effect appears to have reverse effect on economy and slows down the world’s economy and in longer periods causes oil price reduction.

Various studies in electrical energy markets show that the active participation of the demand side in the electricity market, under the title of participation in demand response programs, leads to lower electricity prices, elimination of transmission line density, increasing of network security and improving the liquidity of the market. Accordingly, and in order to get the most out of these programs, it is first necessary to provide an economic model and use it to increase the willing of clients to participate. In this study, a simulation of a economic model of demand response has been performed based on price elasticity of demand and utility function. Since the demand of electricity depends on various elements of decision, such as the price of electricity, level of participation, the value of incentives and penalties specified in the demand response plans, it has been attempted to simulate them in an economical proposal. The proposed model has been evaluated using data related to the city of Tehran. And the results of the simulation have been presented in different scenarios. The results show that time-based rate programs are not sensitive to elasticity, and in any case, changing elasticity is not a determining factor in choosing the optimal policy for market decision makers, and only by changing elasticity, technical and economic indicators can be improved. But incentive-based programs and combined programs are sensitive to elasticity, and changing elasticity, in addition to improving technical and economic indicators, is a determining factor in choosing the optimal policy for market decision makers.

Due to the terrible effects of 2019 novel coronavirus (COVID-19) on health systems and the global economy, the necessity to study future trends of the virus outbreaks around the world is seriously felt. Since geographical mobility is a risk factor of the disease, it has spread to most of the countries recently. It, therefore, necessitates to design a decision support model to i) identify the spread pattern of coronavirus and, ii) provide reliable information for the detection of future trends of the virus outbreaks.

The present study adopts a computational intelligence approach to detect the possible trends in the spread of 2019-nCoV in China for a one-month period. Then, a validated model for detecting future trends in the spread of the virus in France is proposed. It uses the Artificial Neural Network (ANN) and a combination of ANN and Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) as predictive models.

The models work on the basis of data released from the past and the present days from World Health Organization (WHO). By comparing four proposed models, ANN and GA-ANN achieve a high degree of accuracy in terms of performance indicators.  

The models proposed in this study can be used as decision support tools for managing and controlling of 2019-nCoV outbreaks.

One of the most important points in a supply chain is customer-driven modeling, which reduces the bullwhip effect in the supply chain, as well as the costs of investment on the inventory and efficient transshipment of the products. Their homogeneity is reflected in the inventory-routing problem, which is a combination of distribution and inventory management. This paper considers a multi objective IRP in a two-level supply chain consisting of a distributor and a set of retailer. This problem is modeled with the aim of minimizing bi-objectives, namely the total system cost and risk-based transportation cost. Products are delivered to customers by some heterogeneous vehicles with specific capacities through a direct delivery strategy. Additionally, storage capacities are limited and the shortage is assumed to be impermissible. To validate this model, the epsilon constraint method is used for solving the model. Since problems without distribution planning are very complex to solve optimally, the problem considered in this paper also belongs to a class of NP-hard ones. Therefore, a multi-objective imperialist competitive algorithm (MOICA) as a well-known multi-objective evolutionary algorithm is used and developed to solve a number of test problems. Furthermore, the computational results are compared to show the performance of the proposed MOICA.

Organizations usually confront with disruptive incidents. The ability of an organization to reduce the losses caused by these incidents is referred to economic resilience. In this research, the performance of a petrochemical plant is investigated in case of a crisis. At first, based on a comprehensive literature review, a conceptual framework for organizational economic resilience is developed. A structured questionnaire is provided and distributed among the staff of a petrochemical plant as a real case study. Then, an uncertain Data Envelopment Analysis (DEA) model is employed to identify the efficient units of the petrochemical plant. At the end, a sensitivity analysis is performed to examine the importance of each factor in building economic resilience in the plant. This is the first study that presents an integrated qualitative-quantitative approach including a conceptual model for economic resilience and a DEA model in uncertain conditions in the whole supply chain of a petrochemical plant.

Petroleum industry activities can be divided into upstream and downstream sectors. Major upstream activities include exploration of oil reservoirs, drilling and extraction of crude oil, as well as production oil for supply to domestic refineries and export terminals. Refining crude oil and producing oil derivatives are considered in the downstream sector. In this paper, we study optimization of upstream activities of the oil industry and model the crude oil supply chain (COSC) management problem is modeled. The aim of this research is to provide a mathematical optimization model to assist in decision making regarding optimal exploitation of oilfields and COSC management that maximizes the net present palue (NPV) of crude oil production. For this purpose we use a mixed integer linear programming (MILP) model to project strategic and operational decisions for a given time horizon. We use the robust possibilistic programming (RPP) approach to deal with the inherent stochastic or epistemic uncertainty of oil price and demand. Finally, we use the GAMS (CPLEX Solver) software to apply this model and assess its applicability to the National Iranian South Oil Company. The numerical results confirm the applicability of the MILP model, which can be used to maximize the NPV of crude oil for the selected time horizons.

The existing relational network data envelopment analysis (DEA) models evaluate the performance of decision making units (DMUs) with precise data. Whereas in the real world applications, there are many supply chain (SC) networks with imprecise and vague figures. This paper develops a relational network DEA model for evaluating the performance of supply chains with fuzzy numbers. The proposed fuzzy model is capable of evaluating the performance of all kinds of network structures. A pair of two-level mathematical program is utilized to convert the fuzzy relational network DEA to a conventional crisp one. For this purpose, the upper and lower bounds of the efficiencies are calculated by α-cut concept. The proposed model is implemented using actual data from the supply chain of an international shipping company in Iran.

Microgrids not only increase the share of the renewable resources, but also contribute to the realization of sustainable energy development objectives. In the present study, the microgrid is composed of various kinds of power sources, including renewable and nonrenewable resources, along with energy storage system. We modeled a multi-objective mathematical problem to maximize the microgrid’s revenue and at the same time minimize the emission and totat costs while providing a sustainable load. A combination of lexicographic optimization and augmented ε-constraint methods was used to find efficient solutions and to form the Pareto fronties. The best solution among the Pareto optimal solutions was identified using fuzzy decision-making method. The model was implemented at four different days (with different conditions of accessibility to the renewable resources) in Bafrajerd region of Ardebil Province. Finally, the indicate that the proposed model with a comprehensive view on various economic-environmental dimensions of exploiting different production units of the microgrid in a 24-hour time perspective, is an effective and efficient approach for developing, improving, and operation scheduling of the microgrids

From a regional-scale or quick assessment viewpoint, a number of reservoir researchers provided heuristic methodologies to handle the initial technical evaluation of oil reservoirs for carbon dioxide flooding. But, they have not utilized a methodology among multi-attribute decision making approaches, which are more widely accepted and commonly applied. Therefore, this paper aims to introduce the use of a common multi-attribute decision making method, Grey Relational Analysis, for solving the problem of initial technical ranking of oil reservoirs for CO2-flooding EOR. Moreover, the proposed methodology makes some significance to the field of Grey Relational Analysis. The proposed procedure enables oil industry decision makers to consider the parametric uncertainty inherent in the problem by utilizing grey numbers. Furthermore, it serves as a more comprehensive procedure for analyzing real case problems, in which considering different types of criteria seems essential. The methodology is applied to a real case study of Iranian oil reservoirs in order to obtain illustrative results.

Ecological changes resulting from climate conditions can severely affect human societies especially in the area of economy and safety. Climate catastrophes may cause social and economic tension. Forecasting such changes accurately can help the government to control the disasters and to achieve possible benefits (such as water supply in flood). Weather forecasting is the application of science and technology to predict the state of the atmosphere for a given location. Rate of raining is a very important factor in weather forecasting. Different forms of weather forecasting models represent different stochastic processes. Three broad classes of time series modeling in practice are the autoregressive (AR) models، the integrated (I) models، and the moving average (MA) models. These models represent the linear dependence on previous observations. Cyclic variation known as periodic fluctuation or seasonality (S) might be dealt with in time series analysis by using a sinusoidal model. A less completely regular cyclic variation might be considered by using a special form of an auto regressive integrated moving average. In this paper، a hybrid approach based on seasonal auto regressive integrated moving average (SARIMA) method and Locally Linear Model Tree (LoLiMoT) is proposed for forecasting rate of raining. A neural network based on local linear models weighted constructed by a tree algorithm is applied in this research. Training of this network is divided into a structure and a parameter optimization part. A recursive least-squares algorithm is used for training the network since the network is linear in its parameters. A two phase model is developed based on data gathered in Zabol Synoptic Station from 1939 to 2011. In the first phase، the SARIMA model is implemented to predict the raining rate. In the second step neural network based on locally linear model tree is applied to residuals to improve the prediction result. Finally، the proposed model is compared to Sin-Cos model; Result obtained confirm the efficiency of this approach as a practical tool for forecasting the rate of raining.