Topological Resilience Analysis of Supply Networks under Random Disruptions and Targeted Attacks

Abstract

Abstract-Along with the rapid advancement of information technology, the traditional hierarchical supply chain has been quickly evolving into a variety of supply networks, which usually incorporate a large number of entities into complex graph topologies. The study of the resilience of supply networks is an important challenge. In this paper, we exploit the resilience embedded in the network topology by investigating in depth the multiple-path reachability of each demand node to other nodes, and propose a novel network resilience metric. We also develop new supply-network growth mechanisms that reflect the heterogeneous roles of different types of nodes in the supply network. We incorporate them into two fundamental network topologies (i.e. random-graph network and scale-free network), and evaluate their resilience against both random disruptions and targeted attacks using the new resilience metric. The experimental results verify the validity of our resilience metric and the effectiveness of our growth model. This research provides a generic framework and important insights into the construction and resilience analysis of complex supply networks.

Modeling topologically resilient supply chain networks

“…In the context of SCNs, an action is a decision process that a firm uses to select firms that it should supply its materials to. The supply chain literature provides a rich source for potential decision processes (Pathak et al 2009;Xuan et al 2011;Kim et al 2011;Bellamy and Basole 2013;Hearnshaw and Wilson 2013;Wang et al 2015), while providing insights Evaluating Generator Suitability compares a set of synthesized networks to the target using the user-defined structural characteristics in order to determine the representativeness of action matrix M. Finally using Optimization or Learning the action matrix M is perturbed, and a set of best-fit solutions are retained. The process repeats until a termination criterion (e.g., number of iterations) is satisfied regarding how to choose a set of actions that may lead to construction of topologically resilient SCNs.…”

“…Robustness and resilience have thus become important areas of study (for simplicity we refer to both as resilience). While (Thadakamalla et al 2004) was the first to use topology of SCNs for studying resilience, subsequent papers like (Zhao et al 2011b;Wang et al 2015) provide supply chain design insights by examining resilience against both random and targeted attacks. Numerous specialized measures of resilience have also been proposed for supply chains (Barroso et al 2015), but most analyses concentrate on empirical studies from a centralized context.…”

“…it is a supplier, manufacturer, consumer etc. Most supply chain models categorize nodes based on their tiers and roles, and then use these attributes to define attachment rules, for example (Thadakamalla et al 2004;Wang et al 2015) use a heterogeneous preferential linking mechanism that varies based on role of the firm, (Nair and Vidal 2011) proposes an agent-based model that uses firm role and demands to form links, (Wen et al 2012;Mari et al 2015) use restricted preferential attachment mechanism based on firm tiers. Others use more complicated linking mechanisms such as, local selection and preferential attachment (Li et al 2013;Zhao et al 2011b), random, preferential attachment and similarity (Xuan et al 2011), and fitness based attachment (Perera et al 2017c).…”

Assessing supply chain robustness to links failure

“…Typically, scale free/power law networks are considered and both random disruptions a nd targeted attacks are implemented (e.g. Nair and Vidal 2010, Hearnshaw and Wilson 2013, Mari et al 2015, Wang et al 2015. Some of these studies' findings are that denser/more complex networks are not always more resilient and that redundancy may not always lead to greater resilience (Kim et al, 2015).…”

Modeling influence diffusion in networks for community detection, resilience analysis and viral marketing

“…The strategy we take is a novel bottom-up approach [126], in which we adapt our reachability-based influence-diffusion model for community detection to topological resilience analysis of supply networks. More precisely, we exploit the resilience embedded in the network topology by investigating in depth the multiple-path reachability of each demand node to other nodes, and quantify the network resilience by the aggregated resilience of all demand nodes in the supply network.…”