Dissertation Win PV Nguyen (v2).pdf (2.67 MB)

Collaborative Response to Disruption Propagation (CRDP)

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posted on 01.05.2020 by Phuc V Nguyen

Disruptive events during recent decades have highlighted the vulnerabilities of complex systems of systems to disruption propagation: Disruptions that start in one part of a system and can propagate to other parts. Such examples include: Fire spreading in building complexes and forests; plant/crop diseases in agricultural production systems; propagating malware in computer networks and cyber-physical systems; and disruptions in supply networks. The impacts of disruption propagation are devastating, with fire causing annual US$23 billion loss in the US alone, plant diseases/crop reducing agricultural productivity 20% to 40% annually, and computer malware causing up to US$2.3 billion loss per event (as a conservative estimate). These problems, the response to disruption propagation (RDP) problems, are challenging due to the involvement of different problem aspects and their complex dynamics. To better design and control the responses to disruption propagation, a general framework and problem-solving guideline for the RDP problems is necessary.

To address the aforementioned challenge, this research develops the Collaborative Response to Disruption Propagation (CRDP) unifying framework to classify, categorize, and characterize the different aspects of the RDP problems. The CRDP framework allows analogical reasoning across the different problem contexts, such as the examples mentioned above. Three main components applicable to the investigate RDP problems are identified and characterized: (1) The client system as the victims; (2) The response mechanisms as the rescuers/protectors; and (3) The disruption propagation as the aggressors/attackers. This allows further characterization of the complex interactions between the components, which augments the design and control decisions for the response mechanisms to better respond to the disruptions. The new Covering Lines of Collaboration (CLOC) principle, consisting of three guidelines, is developed to analyze the system state and guide the response decisions. The first CLOC guideline recommends the network modeling of potential disruption propagation directions, creating a complex network for better situation awareness and analysis. The second CLOC guideline recommends the analysis of the propagation-restraining effects due to the existence of the response mechanisms, and utilizing this interaction in optimizing response decisions. The third CLOC guideline recommends the development of collaboration protocols between the response decisions to maximize the coverage of response against disruption propagation.

The CRDP framework and the CLOC principle are validated with three RDP case studies: (1) Detection of unknown disruptions; (2) Strategic prevention of unexpected disruptions; (3) Teaming and coordination of repair agents against recurring disruptions. Formulations, analytics, and protocols specific to each case are developed. TIE/CRDP, a new version of the Teamwork Integration Evaluator (TIE) software, is developed to simulate the complex interactions and dynamics of the CRDP components, the response decision protocols, and their performance. The evaluator is capable of simulating and evaluating the complex interactions and dynamics of the CRDP components and the response decision protocols. Experiment results indicate that advanced CLOC-based decisions significantly outperform the baseline and less advanced protocols for all three cases, with performance superiority of 9.7-32.8% in case 1; 31.1%-56.6% in case 2; 2.1%-12.1% for teaming protocols, and at least 50% for team coordination protocols in case 3.


Degree Type

Doctor of Philosophy


Industrial Engineering

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Dr. Shimon Y. Nof

Additional Committee Member 2

Dr. Mario Ventresca

Additional Committee Member 3

Dr. Seokcheon Lee

Additional Committee Member 4

Dr. David Cappelleri