Skip to main content
U.S. flag

An official website of the United States government

Integrating complexity into data-driven multi-hazard supply chain network strategies

October 1, 2013

Major strategies in the wake of a large-scale disaster have focused on short-term emergency response solutions. Few consider medium-to-long-term restoration strategies that reconnect urban areas to the national supply chain networks (SCN) and their supporting infrastructure. To re-establish this connectivity, the relationships within the SCN must be defined and formulated as a model of a complex adaptive system (CAS). A CAS model is a representation of a system that consists of large numbers of inter-connections, demonstrates non-linear behaviors and emergent properties, and responds to stimulus from its environment. CAS modeling is an effective method of managing complexities associated with SCN restoration after large-scale disasters. In order to populate the data space large data sets are required. Currently access to these data is hampered by proprietary restrictions. The aim of this paper is to identify the data required to build a SCN restoration model, look at the inherent problems associated with these data, and understand the complexity that arises due to integration of these data.

Publication Year 2013
Title Integrating complexity into data-driven multi-hazard supply chain network strategies
Authors Suzanna K. Long, Thomas G. Shoberg, Varun Ramachandran, Steven M. Corns, Hector J. Carlo
Publication Type Book
Publication Subtype Conference publication
Index ID 70059279
Record Source USGS Publications Warehouse
USGS Organization NGTOC Rolla