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The influence of Hazardous Materials on different kind of human aspects motivate scholars to increase their attention for decreasing negative impacts of them to human life. In this concern, this paper addresses a decision structure to transfer the hazardous materials to the safe location in the industries logistic system. To reach this goal, we present a new mathematical modelling problem in which location routing problem of hazardous material is addressed. To cope imprecise risk in a practical way, we define it to three kinds in the fuzzy environment including accident risk, population risk, and the bio-environmental risk. To obtain optimal results, three well-known multi-objective evolutionary algorithms (MOEAs) including Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm II (SPEA-II) and Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) are presented for solving ten test problems. In this case, this novelty of the model is discussed, firstly, and then proposed algorithms are compared with respect to obtained results. The results of this paper show that Non-dominated Sorting Genetic Algorithm II (NSGA-II) has superior performance in terms of metrics, and Strength Pareto Evolutionary Algorithm II (SPEA-II) beats other multi-objective evolutionary algorithms in terms of obtained number of individuals in final Pareto solutions. [ABSTRACT FROM AUTHOR]

Abstract: The storage of dangerous substances is a high risk procedure: a historical analysis revealed that 17% of the major accidents associated with the chemical industry are related to this process. When a storage facility is designed, the investment in safety is not always optimal. The safety measures that are applied are sometimes redundant or ill-maintained. One way to improve safety in a storage facility would be to take advantage of the fact that dividing the mass of dangerous substance results in less catastrophic accidents. In this paper, we present a new method for optimizing the design of storage plants and minimizing the risk by calculating the ideal number of tanks and improving the way in which money is invested in safety. This is achieved by redefining how to estimate risk and by applying the principles of mathematical optimization to quantitative risk analysis. The method is explained step by step. We also present two case studies and a validation of the method using risk analysis software and iso-risk curves. [Copyright &y& Elsevier]