Multi-objective optimization of hazardous materials transportation under double uncertainty conditions

Aiming at the optimization of hazardous materials transportation routes, a stochastic optimization model with the goal of the minimum expected value of transportation cost and risk coefficient under the condition of double uncertainty was constructed with the consideration of the uncertainty of the terminal demand on hazmat and population center. A hybrid method of multi-objective particle swarm optimization genetic hybrid algorithm based on priority and combining sample average approximation was designed for solving the stochastic model. Furthermore, an elitist solution set updating method based on dynamic crowding distance was adopted. The feasibility of the model and the effectiveness of the algorithm were verified by example analysis and algorithm comparison. Furthermore, the stability analysis was performed on the results under different sample sizes, and the sensitivity analysis was performed on demand and population. Experimental results showed that the Improved Muti-Objective Particle Swarm Optimization(IMOPSO)algorithm could effectively solve this problem with good convergence and variety. The uncertainties of different sample size, demand and population had impact on the route optimization. The results of transportation route optimization could provide references for decision makers. © 2020, Editorial Department of CIMS. All right reserved.