A random graph model for massive graphs
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Journal of Intelligent and Robotic Systems
Error Detection and Prediction Algorithms: Application in Robotics
Journal of Intelligent and Robotic Systems
Conflict detection during plan integration for multi-agent systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimal and near-optimal test sequencing algorithms with realistic test models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Sequential testing algorithms for multiple fault diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A decision support methodology for dynamic taxiway and runway conflict prevention
Decision Support Systems
| Hi-index | 22.14 |
Conflicts and errors are unavoidable in complex networks such as energy grids and supply chains. The objective of this research is to design algorithms for effective and automated conflict and error prevention and detection. Most algorithms developed earlier, i.e., the traditional algorithm, are centralized sequential algorithms which do not use relationships between conflicts and errors for prevention and detection. A constraint-based model is designed based on the complex network theory to define conflicts and errors and provide prescriptive models for real-world systems. Two algorithms, a centralized algorithm taking advantage of network topology and a decentralized algorithm enabling parallelism with distributed agents, are designed to prevent and detect conflicts and errors. Both algorithms use relationships between constraints to improve efficiency. Analytical study and simulation experiments are conducted to validate the new algorithms and compare their performance to that of the traditional algorithm. Results show that for effective prevention and detection, the decentralized algorithm shall be used according to four performance measures: time, coverage ability, preventability, and damage. If information transmission between agents is disrupted, the centralized algorithm shall be used to achieve better performance than the traditional algorithm. The alignment between algorithms and networks, i.e., centralized algorithms for centralized networks and decentralized algorithms for decentralized networks, improves prevention and detection.