Single AGV Scheduling For Limited Buffer Of Flexible Manufacturing Systems

With the rapid development of China’s modern economy, manufacturing industry has entered the era of intelligent, flexible manufacturing systems controlled by the computer is playing a more and more important role in the manufacturing industry Automatic Guidance Vehicle(AGV) as the main part of transport system in the flexible manufacturing system, for the flexible manufacturing system of flexible operation and accurate control provides a quick and efficient way, so AGV used more widely, how to flexibly schedule AGV become a research hotspot, in order to make the overall performance of flexible manufacturing systems, and save the completion time and cost of the job. This thesis mainly studies input/output buffer with limited capacity in flexible manufacturing system,under this constraint, a single AGV scheduling problem is finishing handling tasks in each processing unit, Mainly includes the following aspects:(1) The overall description of FMS. From the composition of FMS, the definition of handling tasks, input/output buffer management, AGV management and occlusion and scheduling constraints and other aspects in FMS has carried on a comprehensive description and analysis.(2) Single AGV dispatching model with limited buffer is set up. Under the constraint of input/output buffer with limited capacity in FMS, mathematical model has established that a single AGV finish all unfinished handling tasks in the shortest time within a certain time. For the scheduling problem of single AGV, considering the limited input/output buffer capacity, the job to arrive randomness, empty AGV return, at the same time the system maximum allowable quantity of the jobs and the limited resources of the AGV completed impact of handling the task, while setting AGV management rules makes scheduling model created with a certain practicality.(3) Algorithm design and validation. According to the established model, the design of the improved genetic algorithm solve different tasks of FMS model. Improved genetic algorithm is based on the current optimal solution of the design to improve the way of selection, crossover, makes the algorithm in the process of operation is not easy to lose the current optimal solution.(4) Algorithm design and validation. According to the established model, the design of the genetic simulated annealing algorithm solve different Processing units of FMS model genetic simulated annealing algorithm is based on the improved genetic algorithm combined with simulated annealing algorithm to improve the initial population, seek local optimum and remains there until the search of the global optimum.From the practical application of the starting FMS, under the condition of limited input/output buffer capacity in FMS, single AGV completed all tasks in a certain moment to make the time spent in the shortest and set up the model, according to different processing units and the number of task design algorithm for instance validation. Instance results show the feasibility and effectiveness of the algorithm, the improved genetic algorithm come in small scale instances drawn greater probability the optimal solution, with the increase in the number of processing units and tasks, improved genetic algorithm to reduce the probability of the optimal solution, while the genetic simulated annealing algorithm for solving large-scale instances greater probability obtained optimal solution.