The Research On Expressway Pavement Maintenance Decision Optimization Based On Genetic Algorithms

Expressway pavement maintenance decision optimization is a kind of expressway maintenance manager's tool for assistant decision, which helps to make use of limit maintenance funds for keeping pavement stay on the best service level and gaining best economic benefits. It is distinct from the traditional decision mode that depends on human experience and beneficial to the systematism, scientificity and modernization of the pavement maintenance decision, and good for making best use of the pavement resource. There are two optimization solutions to maintenance decision, one is the math programming, and the other is the artificial intelligence which mostly refers to genetic algorithms. Though the math programming is used wildly in EPMS domestically, the solutions are low precise and used a lot of time when the problem is complex at the network level. It is found that the robust search characteristics and parallel handling capability of genetic algorithms are well suited for pavement maintenance decision optimization at the network level. The research of pavement maintenance decision optimization basing on genetic algorithms is conducted extensively internationally; however, the research associated with this aspect is rather limited domestically.There are two kinds of pvament maintence decision optimization, one is the single goal pavement maintence decision optimization, and the other is the multiobjective pavement maintence decision optimization. Firstly, the single goal optimal model of pavement maintenance decision and the multiobjective optimal model of pavement maintenance decision are established after analyzing all kinds of pavement maintenance decision models and its optimization approaches at present. Then a hybrid genetic algorithm is proposed to solve the single goal optimal model of maintenance decision basing on analyzing the advantage and disadvantage of simple genetic algorithms. The hybrid genetic algorithm with characteristics of adaptive pseudo-parallel and optimum maintain is capable of overcoming premature convergence and find global optima efficiently. Moreover, It is proves to be effective through solving the single goal optimal problem of maintenance decision. At last, the current state of the research on the basic theory of genetic algorithms about multiobjective optimization is systematically presented. In addition, great importance is attached to the introduction of the elitist nondominated sorting genetic algorithm (NSGA-II), which is widely used in the problems of multiobjective optimization. At the same time, this method is proved to be more effective than math programming after the multiobjective optimal model of maintenance decision optimized by NSGA-II.However,the hybrid genetic algorithm is not only used in the problems of the pavement maintenance decision optimization of EPMS, but also can slove a lot of optimization problems. What's more, the research on NSGA-II has great significance that NSGA-II is widely used internationally and the research associated with this aspect is rather limited domestically. At last, a new research aspect on pavement maintenance decision optimization domestically is proposed when the pavement maintenance decision optimization is solved by genetic algorithms.