An approach to model reduction of logistic networks based on ranking

Abstr act Simulations or mathematical analysis of a real-world logistic network require a model. In this context two challenges occur for modelling: First, the model should represent the real-world logistic network in an accurate way. Second, it should foster simulations or analytical analysis to be conducted in a reasonable time. A large size is often a drawback of many models. In the case of logistic networks this drawback can be overcome by reducing the number of locations and transportation links of the graph model. In this paper we present an approach to model reduction of a logistic network based on ranking. The rank of a given location states the importance of the location for the whole network. In order to calculate the importance of a location we introduce an adaptation of the PageRank algorithm for logistic networks. The information about the rank and the structural relations between the locations are used for our approach to model reduction. Depending on the structural relation between locations we suggest three different approaches to obtain a model of lower size.