Choosing the task and path for the AGV can be summarized as a task scheduling problem. The mathematical programming method is a traditional method for solving the optimal solution of the scheduling problem. The solving process of the method is actually a optimization process under resource constraints. The practical methods mainly include integer programming, dynamic programming, and petri methods. In the case of small-scale scheduling, such methods can get better results, but as the scheduling scale increases, the time spent solving the problem increases exponentially, which limits the application of the method in responsible, large-scale real-time route optimization and scheduling.
The simulation method simulates the actual scheduling environment to simulate the implementation of a scheduling scheme of the AGV. Users and researchers can use simulation methods to test, compare, and monitor certain scheduling scenarios to change and select scheduling policies. The methods used in practice include discrete event simulation method, object-oriented simulation method and 3D simulation technology. There are many softwares that can be used for AGV scheduling simulation. Among them, Lanner Group's Witness software can quickly build simulation models and realize simulation process. Demonstration and analysis of results.
The artificial intelligence method describes the AGV scheduling process as a process of searching for the optimal solution in the solution set that satisfies the constraint. It uses knowledge representation techniques to include human knowledge, while using various search techniques to try to give a satisfactory solution. The specific methods include expert system method, genetic algorithm, heuristic algorithm and neural network algorithm. Among them, the expert system method is widely used in practice. It abstracts the experience of the scheduling expert into the scheduling rules that the system can understand and execute, and uses the conflict resolution technology to solve the problem of rule expansion and conflict in large-scale AGV scheduling.
4. Because neural network has the advantages of parallel computing, knowledge distribution storage, and strong adaptability, it is a promising method to solve large-scale AGV scheduling problems. At present, the TSP-NP problem is successfully solved by the neural network method. In the solution, the neural network can transform the solution of the combinatorial optimization problem into the energy function of a discrete dynamic system, and obtain the optimization problem by minimizing the energy function. solution.
5. Genetic algorithm is an optimal solution method that simulates the inheritance and variation of natural biological evolution. When solving the optimal scheduling problem of AGV, the genetic algorithm firstly expresses a certain number of possible scheduling schemes into appropriate chromosomes by coding, and calculates the fitness of each chromosome (such as the shortest running path), and repeats replication, crossover and mutation. Look for a highly adaptive chromosome, the optimal solution for the AGV scheduling problem.
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