Steel Supply Chain Complexity and Resilience Assessment: Interactive Network Mapping, Graph Theory, and Agent-Based Simulation

Abstract

Complexity is seen as a major challenge in supply chain research because of interactions, interdependencies, and uncertainties. The main purpose of this study is to develop agent-based models and simulations that focus on Steel Supply Chain Resilience (SSCR) based on complex adaptive network analysis and graph theory. Before and after the simulation, and by mapping the Iranian SSC to the network in the Gephi environment (0.9.2), we used graph theory to analyze node-level and network-level indices. This hypothesis was tested that the corresponding network of the target SSC contained a Complex Adaptive System (CAS). Agent Based Modeling (ABM) has been proposed as a way to track Iran's steel industry supply chain behavior during the crisis using NetLogo (6.2.0). BehaviorSpace, as NetLogo's integrated software tool, was selected for the proposed parameter sweep, design, and experiment execution of agent-based modeling. For sensitivity analysis, the output files were taken from two types of spreadsheets and six scenarios in the table for XLSTAT statistical analysis.