Maximal Benefits Location Model Based on the Fuzzy Function and Analytic Network Process

Abstract

The multiple distribution centers and the demand point’s form are of common interest in the supply chain. In the pursuit of the minimum supply chain cost, the distribution centers in a competitive environment also have the maximum benefit. Therefore, this paper introduces the discrete location model that achieves both the lowest total cost of the supply chain and the maximal effective coverage of the distribution centers. In our model, the right benefit weight and the optimal demand are essential for the final solution. The consumption of demand point is changing, and will cause changes in the inventory cost. We use fuzzy triangular function to better simulate changes in demand, and thus obtain the optimal demand for the demand points. Benefit weight is a multi-parameter product of interaction and interdependence, we use the analytic network process to obtain this. We construct the evaluation index system based on the rational choice of the evaluation parameters, and improve the objectivity of results by reducing the loss of valid information. Using the analytic network process, we establish a network of interdependence and feedback of all parameters and overcome the defects in traditional method where each parameter is independently of each other and deviates from the actual word. The concept of the supply chain in 80 years is as an integrated management of philosophy and ideas. The management is from the supply of goods, ending in the consumption of goods, emphasizing the values throughout the whole process. The distribution centers we have studied in the supply chain play an important role of linking the nodes. They determine the distribution cost of commodities during a certain period of time within some region. This article will point at multiple distribution centers and the demand points as the supply chain nodes. The contribution rate that the demand points contribute to the distribution centers will be transformed into coverage rate based on collaborative inventory of supply chain. And we bring forward the multi-objective maximal covering location model with the variable coverage under the circumstances known to us as the candidate nodes.