Optimization for Risk Decision-Making Through Simulated Annealing

Optimization for Risk Decision-Making Through Simulated Annealing

In this paper, a computational methodology combining the simulated annealing algorithm with two machine learning techniques to select a near-optimal safeguard set for business risk response is presented. First, a mathematical model with four types of risk factor responses (avoid, mitigate, transfer,...

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Bibliographic Details
Main Authors: Marta Lilia Erana-Diaz, Marco Antonio Cruz-Chavez, Rafael Rivera-Lopez, Beatriz Martinez-Bahena, Erika Yesenia Avila-Melgar, Martin Heriberto Cruz-Rosales
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Risk factor to bankruptcy
metaheuristic
machine learning
k-means
decision trees
Online Access:https://ieeexplore.ieee.org/document/9125878/

Internet

https://ieeexplore.ieee.org/document/9125878/

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