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

Area of Science:

• Operations Research
• Network Science
• Urban Logistics

Background:

• Supply chain networks are increasingly complex and interdependent with urban transport systems.
• Understanding the vulnerability of supply chains to transport disruptions is crucial for urban resilience.
• Existing research often lacks detailed network formulations for supply chain-transport interactions.

Purpose of the Study:

• To develop a multilayered complex network formulation for supply chain networks within urban road grids.
• To generate and analyze a dataset of 1,500 supply chain and urban road network instances.
• To empirically investigate the impact of transport network dependence on supply chain vulnerability.

Main Methods:

• Formulation of multilayered complex networks for three supply chain archetypes on an urban road grid.
• Random generation of 500 instances for each supply chain network archetype.
• Calculation of shortest path sets for all 1,500 experimental instances.

Main Results:

• The study provides a robust framework for modeling interconnected supply chain and transport networks.
• Analysis of the generated datasets quantifies the relationship between transport network structure and supply chain vulnerability.
• Empirical evidence highlights specific vulnerabilities arising from supply chain reliance on urban transport infrastructure.

Conclusions:

• The multilayered network approach effectively captures the complex interactions between supply chains and urban transport.
• Supply chain vulnerability is significantly influenced by the characteristics and resilience of the underlying transport network.
• The publicly available datasets facilitate further research into supply chain resilience and urban network analysis.