Abstract
Based on the uncertainty theory, the logistics path optimization of dangerous chemicals is studied in the research. In the course of the research, time-varying parameters, fuzzy variables and fuzzy random variables are introduced to provide quantitative tools for the risk of uncertainty, so that parameters setting and problem hypothesis can better reflect the actual situation of the transport network, so as to eliminate the idealized model. At the same time, the fuzzy simulation is substituted into the genetic algorithm, and the fuzzy stochastic simulation is substituted into the particle swarm optimization algorithm to solve the location-path- dispatching model of dangerous chemicals. On the basis of the traditional heuristic algorithm, the greedy method and the self-adaptive method are used in this paper to improve efficiency of the algorithm and enhance robustness of the results. Finally, as for the small-scale model, exact value is calculated by using Lingo software and changing modes, and the exhaustive method is used to obtain all the optimal solutions. Meanwhile, the improved heuristic algorithm is used in this paper to solve this problem, and then, the comparative analysis is carried out to verify its efficiency and superiority. Finally, the genetic algorithm based on fuzzy simulation and the self-adaptive hybrid particle swarm algorithm based on the greedy method & fuzzy random simulation are proved to be improved effectively.
