On the basis of detailed planning, the smart factory logistics plan needs to be verified through simulation technology in order to optimize and modify the plan. Factory logistics system simulation is to establish a system model for actual logistics operation scenarios, and then perform experiments based on this model, analyze system characteristics on the basis of experiments, optimize system target parameters, or evaluate system operating efficiency. Computer modeling technology is used to build a simulation model to study the problems existing in factory logistics planning, and then optimize the logistics system to avoid bottlenecks in the operation process.
Factory logistics simulation is mainly divided into three categories according to its application scenarios: virtual reality process animation simulation, logistics discrete event data simulation, and logistics system operation simulation. For different application scenarios, different logistics simulation technologies are generally selected. In logistics planning, virtual reality process animation simulation and logistics discrete event data simulation are mainly selected to verify the scheme.
1. Virtual reality process animation simulation
Virtual reality simulation technology mainly displays the physical space location of the logistics system and the relative relationship with other related facilities such as the production line body, as well as the display of factory logistics operation scenes. It is mainly used for plan planning physical space verification, plan introduction and discussion, and external introduction and publicity. .
2. Data simulation of logistics system based on discrete events
Based on discrete event-based logistics system data simulation technology, it mainly studies the calculation of the comprehensive output of the production system and the load of system facilities and equipment under a variety of constraints. Among them, there are many applications in production system layout optimization analysis, production line balance optimization, material distribution plan optimization, job sequencing and production scheduling, and logistics equipment load.
The logistics discrete event simulation is based on the analysis of the structure and process of the logistics system, through the mathematical description of the system, that is, the establishment of a system model, and then through a suitable simulation method, the logistics system is simulated to achieve the process. Through simulation, various statistical properties of the dynamic process of material transportation and storage can be understood, such as whether the utilization rate of transportation equipment is reasonable, whether the transportation route is unobstructed, and whether the flow cycle of the material handling system is too long.
3. Logistics system operation simulation
The logistics system operation simulation mainly studies the operation analysis of the production logistics system driven by the basic data of the factory operation and the information system, such as the rationality verification of the information system design and development (the feasibility of logic and algorithm) in the new system development and verification stage; During the operation stage of the factory, evaluate the rationality of the daily operation scheduling plan.
Logistics system operation simulation is a logistics system simulation model based on the operation plan. Run the entire production logistics system by modeling the entire production process of the factory and driven by the operation plan of scheduling systems such as APS, using the production environment resources of manufacturing execution systems such as MES as constraints and combining the dynamic scheduling strategy of random logistics events. Simulation model, and analysis and optimization process. Through a large number of interviews and experiments, the planned scheme was adjusted and optimized to optimize the operation efficiency of the factory. In the operation phase, the production logistics simulation model can also be extracted at any time, and then deduced for some scenes of the production operation, and accurately calculated some related index parameters to predict the operation of the factory, thereby providing a decision for the operation and management of the factory Decision support and basis.
