Research On FMS Logistics System Based On The Agent Model

With the fierce market competition, enterprises have put more energy and resources into raw material exploration and productivity improvement of their familiar core areas. Reducing the operation cost and getting more profit are becoming the key of businesses development, is a kind of automationtechnology which is based on computer software and applied to multi-type low-volume production. Its major advantages are high facility utilization, short production cycle. And production methods are flexible. In this way,production efficiencyand economic benefits can be increased greatly.However, at the same time, flexible manufacturing system has complex composition, too many requirements for performance index and uncertain optimization methods. Those problems has restricted the development of FMS. As a result, the study of production logistics systems plays an important role in optimizing the flexible manufacturing system. This paper starts with the production of FMS and adopts the technology of surrogate models and ant colony optimization to optimize and simulate this system, so as to achieve the purpose of improving productivity efficiency.First of all. the author introduces FMS studies at home and aboard and summarizes the current study situation of technology of surrogate models and ant colony. The author also builds FMS visualizationmodels by studying logistics system. setsreasonable preferences and attempts to analyze this flexible manufacturing system.Secondly, according to processing rate and clogging rate, the author conducts experiments on desk processing system. By analyzing the datum of pie charts and consulting requirement of eliminating bottlenecks and lowering clogging rate, the author tries to put forward suggestions for improvement. After that, the author compares the former and latter result and finds that the latter production efficiency has improved. In addition, configuration of processing system has become more reasonable.Thirdly, by analyzing the experiment data, the author determines six parameters as variables to build surrogate models. And he gains fifty groups of experiment datum and fifteen groups of error analysis samples by use of Latin hypercube sampling. What’s more, the author builds three kinds of surrogate models. They are highest production efficiency, best utilization of facilities and lowest clogging rate surrogate model. He analyzes datum of surrogate models by use of Isighterror method to meet the requirements for precision.At last, the author selects the highest production efficiency as objective function on the basis of physical models which built on response surface to optimize six variables by genetic algorithm. In these ways, the author reduces average clogging rate and increases the utilization of facilities.what’s the most important is that, with those methods, the author manages to improve production efficiency.