| Final automobile production line included many processes and the whole system is very complex. There are many random factors in the production system, such as shutdown and repair. The efficiency of the system will be greatly influenced by design of the final automobile line. In this paper we research and apply the discrete-event simulation in the design of the final automobile line. After complete the model’s verification and validation, statistical method was applied to analysis the system.We first discussed system simulation and related technology based on the research of the automobile production line simulation in domestics and overseas. By analyzing the layout of the system we set the objective of the project. Secondly we fitted the distribution of the historical data and determined the distribution type and its parameters. The simulation model was developed by the AutoMod simulation software which needs to be in-depth studying. In order to increase daily output, we analysis the final automobile production line simulation model and present the point estimation and interval estimation value. In the end several experiments were conducted and optimal system design was given.During the analysis phase statistical method was adopted to analysis the simulation results after complete model verification and validation. Based on five random samples, the average productivity each day is1107, the standard deviation is9.57, and middle value is1188. Under confidence level of90%, the productivity of final production line each day is [1090.88,1113.12]. Based on this data, this paper can get the conclusion that the throughput of this system can meet the requirement and there are no bottlenecks in the system.Running rate, number of equipment and buffer size are the top three important factors in the system, so several experiments were conducted to determine the optimal value of those factors. This paper gives quantitative decision making tool to system designer. According to the experiment data, we can come to the conclusion that the number of skid board and capacity of the buffer have great influence on the productivity each day. According to the experiment results, the number of skid and capacity of buffer are two most influential factors. However capacity of buffer is more sensitive than the number of skid. Under certain running rate, adding two buffer locations can make the system optimized. |
