Multilayered complex network datasets for three supply chain network archetypes on an urban road grid

Abstract

This article presents the multilayered complex network formulation for three different supply chain network archetypes on an urban road grid and describes how 500 instances were randomly generated for each archetype. Both the supply chain network layer and the urban road network layer are directed unweighted networks. The shortest path set is calculated for each of the 1 500 experimental instances. The datasets are used to empirically explore the impact that the supply chain's dependence on the transport network has on its vulnerability in Viljoen and Joubert (2017) [1]. The datasets are publicly available on Mendeley (Joubert and Viljoen, 2017) [2].

The Road most Travelled: The Impact of Urban Road Infrastructure on Supply Chain Network Vulnerability

“…(The algorithms used to generate the datasets described in this section are detailed in Viljoen and Joubert (2017) and the datasets themselves are publicly available on Mendeley (Joubert and Viljoen, 2017)) For each of the three network archetypes G 1K , K ∈ {F, S, D}, we generate 500 instances of M with N 1K = 12 for all three archetypes and G 2 a 10×10 square grid as shown in Figure 1b. The difference between the instances of M is the random association of X 1K to nodes in X 2 .…”

“…The lower mean and wider spread in both tails of the Double Hub is explained by the structure of G 1D . Intra-hub paths (paths between nodes that share a common hub node) in the Double Hub are generally shorter than the paths of the Single Hub because in the former the placement of the two hubs on the grid effectively splits the grid and therefore the intra-hub nodes are closer to their respective hubs (Viljoen and Joubert, 2017). On the other hand, the inter-hub movements are longer as they have to be rerouted through two hubs.…”