Purpose: This study aims to use big data technology to predict the risk probability of hazardous chemicals logistics roads, convert the risk probability into a cost, and establish a model with the goal of cost minimization. Research design, data, and methodology: The objective function is composed of three parts: vehicle cost, transportation cost, as well as risk cost. An optimized ant colony algorithm is proposed to solve this model and to compare the difference in cost incurred b y multi-type t ransportation and s ingle-type t ransportation. During the experiment, A regional hazardous chemical logistics company is an example. Results: The results of the model solution show that the use of big data techniques to predict the risks on the logistics transportation path of hazardous chemicals, while taking into account transportation safety and logistics costs, and improving the transportation safety of hazardous chemicals, the comparison between multi-type transportation and single-type transportation highlights the advantages of multi-type transportation. The advantages are more in line with the actual operating conditions of logistics companies, and the optimized ant colony algorithm achieves better performance and convergence speed than the basic ant colony algorithm in terms of optimal solution and convergence speed. Implications: Thus, it has certain reference value for hazardous chemical logistics companies to choose transportation solutions.
Han et al. (Sat,) studied this question.