Optimization for Risk Decision-Making Through Simulated Annealing

Abstract

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.

Optimization Method to Address Psychosocial Risks through Adaptation of the Multidimensional Knapsack Problem

“…It is ideal for large PSR optimization problems. This behavior allows the SA algorithm to be incorporated into the technology platform with the enterprise security methodology [60][61][62]. On the other hand, as shown for small instances, the SA-high scheme presents greater precision, where its implementation brings great benefits.…”

Transient Storage Model Parameter Optimization Using the Simulated Annealing Method

“…It was first used for fitting the parameters for the equation of state for substances consisting of interacting individual molecules (Metropolis et al, 1953). The simulated annealing method has been applied to a large variety of optimization problems, including hydraulic parameter estimation (Rucker, 2011), decision making (Erana-Diaz et al, 2020;B. Li et al, 2019;Wang et al, 2019), resource allocation (Aerts & Heuvelink, 2002;X.…”

“…It was first used for fitting the parameters for the equation of state for substances consisting of interacting individual molecules (Metropolis et al., 1953). The simulated annealing method has been applied to a large variety of optimization problems, including hydraulic parameter estimation (Rucker, 2011), decision making (Erana‐Diaz et al., 2020; B. Li et al., 2019; Wang et al., 2019), resource allocation (Aerts & Heuvelink, 2002; X. Li & Ma, 2018), hazard assessment (Hackl et al., 2018; Hosseini et al., 2020), and GIS spatial optimization (Cruz‐Chavez et al., 2020). We hypothesize that simulated annealing may be a feasible approach to determine the global minimum parameter vector for hyporheic zone modeling using the TSM.…”

Simultaneous assessment of health, safety, and environmental risks using William Fine and FMEA methods based on OHSAS 18001: 2007 standard in the Alborz tunnel, Iran

“…In addition to reducing the risks of threatening factors and maintaining people's health, utilizing different methods of risk assessment also leads to improving the economic indicators of the project [14,15]. Meanwhile, the FMEA method is one of the effective tools used in the tunnel construction industry and systematically identifies and implements activities that can reduce and eliminate the occurrence of potential errors [16].…”