Ordering and transportation of raw materials for manufacturing enterprises

Abstract

As the necessities of production and life, construction and plate have always played an important role in China's national economy. It is also an important industry affecting China's economic development. The unit price of raw materials and the cost of transportation and storage have always been the main expenses of enterprises. Therefore, it is particularly important to study how to reduce the ordering cost and transshipment cost of raw materials. This paper makes a quantitative analysis on the supply characteristics of 402 suppliers. According to the supply quantity of suppliers, the orders with an order quantity of 0 are excluded, so as to extract the variance, range and real average value of each supplier's orders in five years. The difference between the enterprise's order quantity and the supplier's supply quantity is extracted, so that they represent four indicators: supplier reliability, sustainability and stability, And the supply intensity of the supplier. After the data is normalized through AHP, the weight of the index is assigned, the index is weighted in combination with TOPSIS, and finally the score is calculated. Then, the 0-1 programming model is established. The coefficient of the objective function is the maximum supply of each supplier every week. After analysis, it is concluded that the maximum supply is two-thirds of the average value plus one-third of the maximum value. The constraint conditions are established so that the sum of the supply of the selected suppliers is greater than the weekly capacity required by the enterprise. The 240 weeks are divided into five years. Considering the influence of practical factors, when predicting the supply of suppliers in the next 24 weeks, only the first half of each year is selected, and the grey prediction method is used to predict the average weekly supply of each supplier in the next 24 weeks. After the prediction, the grey prediction of the existing data is not ideal, and the BP neural network is used to re predict this kind of data. After the prediction, a multi-objective programming model is established to ensure the minimum sum of ordering cost and transshipment cost. Under this scheme, the minimum supply corresponding to a, B and C is calculated.