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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, and accept) is constructed. Then, the simulated annealing algorithm is applied to find a set of near-optimal solutions to the model. Next, these solutions are processed by the k-means clustering algorithm for identifying three categories, and with a decision tree classifier, the most relevant elements of each one are obtained. Finally, the categorized solutions are shown to the decision-makers through a user interface. These stages are designed with the aim of the users can take an appropriate safeguard set and develop one specific and optimal program to respond to business risk factors. The results generated by the proposed approach are reached in a reasonable time using less computational resources than those used by other procedures. Furthermore, the best results obtained by the simulated annealing algorithm use a lower business budget, and they have a relative-error less than 0.0013% of the optimal solution given by a deterministic method.
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, and accept) is constructed. Then, the simulated annealing algorithm is applied to find a set of near-optimal solutions to the model. Next, these solutions are processed by the k-means clustering algorithm for identifying three categories, and with a decision tree classifier, the most relevant elements of each one are obtained. Finally, the categorized solutions are shown to the decision-makers through a user interface. These stages are designed with the aim of the users can take an appropriate safeguard set and develop one specific and optimal program to respond to business risk factors. The results generated by the proposed approach are reached in a reasonable time using less computational resources than those used by other procedures. Furthermore, the best results obtained by the simulated annealing algorithm use a lower business budget, and they have a relative-error less than 0.0013% of the optimal solution given by a deterministic method.
Selection of the most efficient algorithm for a given set of linear programming problems has been a significant and, at the same time, challenging process for linear programming solvers. The most widely used linear programming algorithms are the primal simplex algorithm, the dual simplex algorithm, and the interior point method. Interested in algorithm selection processes in modern mathematical solvers, we had previously worked on using artificial neural networks to formulate and propose a regression model for the prediction of the execution time of the interior point method on a set of benchmark linear programming problems. Extending our previous work, we are now examining a prediction model using artificial neural networks for the performance of CPLEX’s primal and dual simplex algorithms. Our study shows that, for the examined set of benchmark linear programming problems, a regression model that can accurately predict the execution time of these algorithms could not be formed. Therefore, we are proceeding further with our analysis, treating the problem as a classification one. Instead of attempting to predict exact values for the execution time of primal and dual simplex algorithms, our models estimate classes, expressed as time ranges, under which the execution time of each algorithm is expected to fall. Experimental results show a good performance of the classification models for both primal and dual methods, with the relevant accuracy score reaching 0.83 and 0.84, respectively.
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