Swarm Intelligence Investigation of a Risk Management Model
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Abstract
The virtual enterprise is undoubtedly exposed to various risks from multiple angles owing to its dynamic operational setting, diverse constituents, and dispersed characteristics. Identifying crucial information that establishes a connection between the owner and the partners represents a potential gap that impedes sound decision-making. Therefore, the significance of such information cannot be overstated in managing the risks associated with a virtual enterprise. Each partner involved in a virtual enterprise is susceptible to various risk factors that threaten the enterprise's overall integrity. As the number of participants, risk factors, and events within a virtual enterprise increase, the search space will experience a significant expansion. The model under investigation pertains to a distributed decision-making process within virtual enterprise risk programming. Numerous control strategies are available for mitigating each risk. This research aims to identify a suitable decision-making methodology that can enhance the overall effectiveness of risk management practices across the organization. The present study uses the Grey Wolf Optimizer (GWO) to acquire valuable solutions. The findings indicate that the algorithm operates highly and that the model augmented the linkage between the owner and the partners. A comparative analysis is performed to evaluate the impact of the algorithm on mitigating risks associated with virtual enterprises, as compared to prior findings. The results indicate a positive effect of the algorithm in reducing such risks.
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