محمدتقی تقوی فرد

INTRODUCTION: 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). METHODS: 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. FINDINGS: 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. CONCLUSION: 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.

This study aims to present a model for predicting corporate default among Tehran Stock Exchange’s selected industries. To do this, corporate default drivers were identified and selected by referring to previous research findings and using experts’ opinions. These drivers were divided into five categories: accounting ratios, market variables, macroeconomic indicators, nonfinancial factors, and earnings quality measures. Structural equation modeling (SEM) technique was used to derive the prediction model. In this technique, corporate default drivers were used as latent independent variables, and their constituent factors were considered as observable indicators of the above variables. In addition, corporate default, as the latent dependent variable, was calculated by a measure based on the Black-Scholes-Merton (BSM) option pricing model. After implementing structural equation modeling (SEM) technique by use of Smart PLS software, a prediction model that contains influential drivers of corporate default was derived and presented for each of the selected industries.