Application Research Of Genetic Algorithms In Shop Scheduling

AI Scheduling is one important research direction in artificial intelligence (AI) field. Although it almost started as early as AI planning, which began in 1950s, it was very late for people in 1980s to attach great importance to it. AI Scheduling has great similarity and relevance with AI planning, so lots of scholars have also considered it as a special case of AI planning and applied it into the research so that most of the relevant mature algorithms such as CSP,Heuristic Search,Genetic Algorithms and nerve network have been used to solve AI Scheduling Problem successfully ,which lead to the rapid development of AI Scheduling in the late twenty years of last century..Job-Shop Scheduling problem is one of the most difficult ones in AI Scheduling, the model of which being widely used to solve the application problems, such as: shop product line management, logistics transportation, timetable arrangement and human resource planning. Because of these practical meanings, the research has lasted more than half a century without intermitting. And a lot of excellent arithmetic for Job-Shop problem, such as: branch definition, Priority Dispatching Rule, Local Search, Heuristic Induction and Constraint Satisfaction. But there are certain merit and shortcoming for any arithmetic. currently its achievements of shop scheduling in theoretical research mostly focus on the Scheduling Problems with the representation of JSP on the basis of the minimum of makespan.Genetic Algorithms is a kind of random searching technology , its application research in shop scheduling develops in recent years, the following thesis will carry on the analysis on the concrete shop scheduling problem ,uses the tool such as Genetic Algorithms to solve shop scheduling problem , achieves its computational simulation over this Algorithms and puts forward a feasible plan which can get satisfied results quickly.