Routing Optimization For Hazardous Materials Transportation Under The Condition Of Uncertainty

With the rapid development of modern industrial society, hazardous materials has become the indispensable materials of agriculture, industry, defense and daily life. Hazmat transportation accidents are perceived as low probability-high consequence events. Once the hazardous materials accident occurs, it can led to the huge loss to life, property and environment. Ensuring the social security and reducing risk are the key problems.This paper studies the shortest path problem and multi-depot vehicle rout ing problem with hazardous materials. First, we study the hazmat transportation risk model and credibility theory. Second, we present the shortest path problem and multi-depot vehicle routing problem combined with credibility theory. Finally, hybrid intelligent algorithms integrating fuzzy simulation are designed for finding satisfactory solutions. Some numerical examples are given to demonstrate the efficiency of the proposed models and algorithms. The study provides theoretical support and decision for hazmat transportation.The research contents of this paper are as follow(1) The study of hazmat transportation risk measure, analysis and credibility theory. Combining with the home and abroad status of hazmat transportation risk analysis methods, this paper aims to lay a theoretical foundation for formulating hazmat transportation models.(2) The establishment of the optimization model of hazmat transportation routing. This paper considers the shortest path problem and multi-depot vehicle routing problem. Based on the shortest path problem, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. We formulate a chance-constrained programming model within the framework of credibility theory. Based on the multi-depot vehicle routing problem, a fuzzy bilevel programming model is formulated to minimize the expected transportation risk, in which the upper level allocates customers to depots under the constraints of depot capacities and customer demands, and the lower level determines the optimal path for each group of depot and customers.(3) Solution method for models. Based on the shortest path model, we use crisp equivalents and a hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm are designed for finding a satisfactory solution. Based on the multi-depot vehicle routing problem, four fuzzy simulation-based heuristic algorithms are designed to search the optimal solution.(4) The design of multiple cases of solving two models. Illustrative examples are given to demonstrate the efficiency of the proposed model and compare the performance of these heuristic algorithms.