As an indispensable part of citizens daily life, Hazardous materials transportation (Hazmats) not only plays an increasingly important role, but also brings a series of problems, the most prominent one is the safety problem. When it attempts to find a vehicle route scheme which can obtain risk minimum in a fuzzy environment for a single warehouse, carbon emission cost is also been taken into account, which is, green vehicle route model (GVRP) for hazardous materials. In this paper, a new methodology is used in the modeling process. First, the vehicle travel distance and the population density along the road are assumed as fuzzy variables. Furthermore, on the basis of risk model, we formulate a chance constrained bi-objective programming model within the framework of credibility theory, one goal is to have the lowest total cost including the green cost, the other goal is to have the lowest total risk of the vehicle route scheme, then the main purpose is to establish a balance between cost and risk. Then, it designs two hybrid intelligent algorithms, which are the combination of fuzzy simulation algorithm and genetic algorithm, particle swarm optimization algorithm, respectively, and proves the convergence of the two algorithms. Finally, numerical examples are given to illustrate the effectiveness of the proposed model and algorithms.

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