Fuzzy Regression Model to Determine the Demands of Distribution Networks under Uncertainty Case Study: Supply Chain Isaquo

In the current competitive, integrated approach to supply chain management and distribution network design, especially in conditions of uncertainty, in order to ensure timely needs of customers in terms of savings in costs and raise the level of customer service, in recent years. Estimating the stochastic demand for transportation distribution networks is a crucial factor in transport planning problem. The purpose of this kind of problem is to find a permutation of customer demand that the penalty of losing a customer to a minimum. Virtually every customer demand depends on various parameters, the parameters in the real world, sometimes with uncertainty. Fuzzy model is a powerful tool for decision-making in fuzzy environment. Human has a good ability to process qualitative data, which helps decide fuzzy environment. In many practical cases, decisions with uncertainty and in this case the power are not numeric input and output data. Uncertainty using by vector of dependent variables as demand vector which model each of these in turn depends on the independent variables. However, in many practical cases, customer demands are unclear. The method of least squares regression phase in this research as a powerful tool for decision-making in a fuzzy environment. The methodology involved using least square approximation-distance method to estimate random distribution network demands Isaco as a case study. Least Squares Approximation-distance estimator is applied to the data. The SSR values of the Least Squares Approximation-distance estimator and real demand are obtained and then compared to SSR values of the nominal demand and real demand. Empirical results showed that the proposed method can be viable in solving problems under circumstances of having vague and imprecise performance ratings. The results further proved that application of the Least Squares Approximation-distance was realistic and efficient estimator to face the stochastic demand challenges in supply chain management and distribution network design, especially in conditions of uncertainty management and solve relevant problems.