Economic optimization of industrial safety measures using genetic algorithms

The paper presents a computer-aided methodology for economic optimization of industrial plants safety. The method is based on the minimization of total safety-related cost including investment, operating expenses of adopted safety measures, and expected monetary loss from accidents. The objective function minimization is pursued resorting to a genetic algorithm which selects the best mix of safety measures able to attain the optimal risk level at minimum cost by factoring in the cost and risk reduction potential of each candidate safety measure. After a detailed description of the optimization approach the paper discusses two numerical examples showing the method application to both easy and complex decision making scenarios.

► We model the problem of selecting safety measures in industrial plants. ► Safety measures are chosen to minimize the total safety-related cost. ► A genetic algorithm is developed to solve the related combinatorial optimization problem. ► Method helps plant managers to allocate economic resources in complex decision making scenarios.