seyyed mohammadtaghi taghavifard

The aim of this study is to provide a model for sourcing in the supply chain of the poultry industry. The research in terms of audience is basic, in terms of purpose is descriptive and from the time perspective is cross sectional. The research approach is mixed (qualitative and quantitative). The collecting data in the qualitative part was semi-structured interviews and in the quantitative part was Delphi questionnaire and Likert scale. The statistical population in the qualitative and Delphi section included  12 managers of the poultry industry who were selected by purposive sampling. The statistical population of the quantitative part of the research were the middle managers and senior experts of poultry industry. We used Cochran’s formula   and selected 384 persons by simple random method. We used a thematic analysis for collecting data and MAXQDa software for analyzing interviews.  In the quantitative part, the partial least squares method were utilized using Smart PLS software to validate the model. The research findings showed that the enablers of sourcing and supply chain management affect the mechanism of cooperation, selection and evaluation of supply sources. These structures in turn affect the sourcing strategy of the supply chain and the sourcing strategy also affects the quality, price, environmental and delivery performances. These structures, by affecting risk management in sourcing, ultimately improve sourcing in the poultry industry.

In times of crisis, the timely transfer of the injured to medical facilities is one of the most important stages of relief and one of the most widely used methods to achieve the transfer point designing goal. The transfer point in literature is a place to collect and transfer the optimal demand for a particular service. For example, in times of natural disasters such as earthquakes, the injured (customers) are transferred by ambulance to the transfer points and then by helicopter to the hospital (facility).

In this study, two single-objective and double-objective complex integer number programming models were presented for the problem of locating transfer points and optimal allocation to facilities, taking into account the limitations in facility capacity and transfer points as well as assuming two types of normal and bad injuries.

In the single-objective model, the reduction in the time of sending the injured in the relief chain, and in the double objective model, in addition to the previous goal, the reduction of the fine for not sending the injured were examined. It is only possible to transfer each injured person to the hospital using the transfer points, and the treatment of the normally injured individuals is performed at the transfer points. The models were solved with two approaches, mild and severe. In order to show the efficiency of the proposed models, a case study was conducted in districts 10, 11, and 17 of Tehran metropolis, Iran.

Setting up transfer points has a great impact on speeding up the process of providing services to the injured. Additionally, given the disproportionality of the number of injured with the capacity of hospitals in severe crises, it is necessary to anticipate transfer points to manage relief and respond to all injured.

Sustainable development is based on knowledge and education is the key of knowledge and consciousness development. So it could be noted that: education is the key of development. Here it is necessary to denote the efficiency of educational courses to achieve goals of the organization. Therefore the main Purpose of research is addressing the Return on Investment (ROI) of education in urban development area in Tehran municipality. In this study, special trainings of Tehran municipality urban development and its ROI was investigated. In this process AHP teaching and expert Choice software was about 559.37 percent. This Rate is a well ROI rate and shows the logic holding of these courses. Also it shows that investing in education in Tehran municipality will have a positive effect on its total productivity, in long term.