Research On FMS Evaluation With Fuzzy Parameters Based On DENFORD

With the market demand being more and more various, the flexible manufacturing system (FMS) which adapts well to multi-item and medium&small batch production is playing a more and more important role in modern industry. Evaluating the production performance of FMS accurately is of great significance for the reasonable formulation of production plan and the improvement of the utilization of processing units and the productivity of the system. The stochastic Petri net (SPN) is an important tool for the performance analysis of FMS.In traditional SPN analyzing method of manufacturing systems, considering the randomness of operation time, firing rate parameters which obey exponential random distribution are used to describe the time variables in the manufacturing process in PN models. However in actual production, as a result of the influence of the measuring technology and the subjective factor of the gauger on the measuring process of time parameters, the accuracy of data can’t be guaranteed in most of the conditions. It’s hard for the stochastic theory to describe well the fuzziness of data in the measuring process, so ordinary SPN can’t reflect the real properties of manufacturing system. On the basis that the theory of traditional SPN performance evaluation of manufacturing systems is analyzed, pointing at its shortcoming that the fuzziness of data got in the parameters measuring process can’t be described, the method of system performance evaluation using SPN with fuzzy parameters is proposed to model and analyze FMS, the specific theory and procedures are also studied. The performance evaluation of DENFORD FMS is set as an example, the structure and operating principle of the system is analyzed and its PN model is built. Ordinary SPN and SPN with fuzzy parameters are used respectively to solve the model, and its main performance indexes are evaluated according to the solving result. The advantage of performance evaluation method with fuzzy parameters is indicated through the evaluation results comparisons of the two methods. The DENFORD FMS is modeled and simulated under the environment of ShowFlow, through the simulation results of which the reliability of fuzzy-parameter evaluation theory is verified furtherly.The application of fuzzy-parameter evaluation theory in the performance evaluation of DENFORD FMS shows that, this method can consider the randomness and fuzziness of time parameters in the manufacturing process comprehensively, with which the performance evaluation of FMS is more precise and the evaluation results are of certain value for reference.