مرتضی هنرمند عظیمی

The core goal of this study was to simulate the share and predict the current and non-current receivables in Shahr Bank using an agent-based approach. To this end, we employed the data related to the examined features in 11 bank branches aimed at determining the degree of importance of each of the factors affecting the repayment behavior of borrowers using the C5.0 algorithm, the decision tree method, and the simulation process in NetLogo software. We utilized the comparison of the repayment status of a sample of 100 customers over the past year (12 months ago) and at present aimed at assessing the simulated model. The estimation results of the simulation model, as well as the real findings, suggested that the number of type 1 customers (with active repayment status) has decreased during the last 12 months and the number of type 2 customers (with past due repayment status) has enhanced. If the economic recession conditions continue the same way, and considering the low effect of the recession conditions on the businesses of the bank''s customers in the next four years, then, the ratio of the bank''s current customers will decrease from about 89% to about 81%, while the ratio of the past-due customers of the bank will increase to more than double to about 13%. In the next step, we tried to simulate the future situation of the ratio and the share of the current bank customers'' claims in the next 4 years.

Innovation capability refers to a complete set of characteristics of the organization that facilitates innovation strategies, and the food industry plays a pivotal role in the processing of agricultural raw materials and food supply. The aim of the present study was to Identify and Analyze of the dimensions of innovation capability, clustering and ranking indexes in Urmia food industries. The sample size of the statistical population encompassing 221 companies was equal to 143 according to Cochran's formula. Obtaining required information was based on field study method. In the applied part of the study, exploratory factor analysis, clustering with K-MEANS algorithm and cluster ranking based on AHP technique were used. The results of the exploratory factor analysis showed that the relationship of each item with the factors classified into three categories was properly expressed. Companies with similar features but different from other clusters were put into 8 clusters. Finally, according to the weights of study variables, "human", "operability" and "structural” factors ranked first, second and last respectively.