01593349

Component

Advances in the complex network theory have recently been reflected within the field of supply chain network modelling. In particular, insights into supply chain network resilience have been gained by generating network topologies from various growth models. Since the given growth model governs the topology and the resulting resilience characteristics of the network, it is imperative to adopt a growth model which represents the real world supply chain network firm partnering process as closely as practically possible. Consideration of such detail ensures accurate insights into the resilience characteristics of real world supply chain networks. Thus far, the growth models used to generate supply chain network topologies have only considered the node degree to derive the attachment probability of new nodes to the existing nodes in the network. It is argued here that the attachment probability should include a more basic parameter, referred to as node fitness, which reflects the intrinsic attributes of the nodes, including or excluding the node degree. This paper presents two fitness-based growth models from the complex network literature, which can be adopted to characterise the formation of supply chain networks by capturing the fitness distribution of nodes through a given probability distribution function with a specified standard deviation. The simulation results indicate that the network topologies generated from fitness-based growth models include distinctly different resilience characteristics when compared with the network topologies generated degree-based growth models. In general, the network topologies generated by a purely degree-based growth model tend to overestimate the resiliency of supply chain networks.

This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.

01584066

16-3264

English

20p

2016

Transportation Research Board 95th Annual Meeting

Digital/other

Figures; References; Tables

Distributions (Statistics); Freight traffic; Network nodes; Simulation; Supply chain management

Freight Transportation; Planning and Forecasting

Transportation Research Board Annual Meeting 2016 Paper #16-3264

TRIS, TRB, ATRI

Jan 12 2016 5:27PM