Uncertainty Theory Based Optimal Decision Making Research Of Hazardous Materials Transportation Network

The massive usage of hazardous materials (referred to hereinafter as HAZMAT) has promoted the rapid development of the industry greatly, and has made a great contribution to the development at full speed of the world economy. However, the inherent hazard characteristics of HAZMAT would very likely cause catastrophic damage to various links of the whole supply chain. Transportation is the key link constituted a serious threat, although the accident rate has the characteristic of low probability. As we all known, the consequences of the accidents are of "high risk" in the nature, not only to people's lives, but also to the property safety and public safety. Therefore, the transportation including HAZMAT has become a more and more popular research issue. In the business of HAZMAT transportation, there are many problems on decision-making optimization, which are mutual restraint and then cannot be studied and concluded separately. In general, the primary task of HAZMAT transportation is to ensure safety, on this basis, and then how to reduce costs or increase economic benefits should be considered.In view of the risk of HAZMAT transportation network to the public safety, and many uncertainties contained in the process, this paper will study the location of the candidate depots, routing and vehicle scheduling problems at the same time. The main research contents are as follows:(1) Firstly, this paper describes the current situation of China's HAZMAT transportation, combined with domestic and foreign literature to explore the depth and breadth, maturity and deficiency of present research in this field, so as to seek breakthrough and innovation;(2) In order to clarify the purpose and methods of the research to reduce the risk and reduce the total cost, the optimization models combined these two goals would be set up;(3) In this paper, the time-varying parameters, fuzzy variables and fuzzy random variables are introduced to provide quantitative tools for uncertain risks, such that the setting of parameters and the assumption of problems can be more realistic, as far as possible to improve the idealized model;(4) The improved genetic algorithm and particle swarm optimization algorithm are used to solve the models. Based on the traditional heuristic algorithm, this paper applies the greedy method and the adaptive method to improve the efficiency of the algorithm and the robustness of the results;(5) For the small examples, we use lingo software to solve one by changing the form of the model, also, we design a exhaustion method to solve the other one. The income of the exact optimal solutions are compared with results by the improved heuristic algorithm, which shows that the designed heuristic algorithm is efficient and superior. At the same time, the general heuristic algorithms are used to solve the examples to make comparison, which proves that the heuristic algorithm is improved significantly. And finally, we obtain relevant conclusions of the four numerical examples.