Based On The Lean Thinking Of The Magnet Production System Modeling, Simulation And Optimization

In order to improve product quality, increase market share, mangy manufacturingcompanies have introduced and implemented Lean Production to eliminate waste,shorten production cycles, improve operational efficiency and achieve on-time delivery.In this paper, the study object is the core departments of a large medical equipmentmanufacturing company--magnet production system. Starting from the perspective oflean production ideas, combined with computer simulation technology, the authoranalyzes and optimizes production systems, improve the efficiency of the system,reducing system operating costs. The work is as follows:1. Describing the core idea of lean production and analyzing the discrete eventsystem modeling and simulation theory. And analysis the main problems in the magnetproduction system and the design framework of the modeling simulation based on leanproduction is gave.2. Briefly describing the concept, objectives and several typical types of layout.To research the magnet production system, using Em-Plant software to build thesimulation model and based on the minimum material handling volume, the model isoptimized and got the reasonable production layout, greatly reducing the amount ofmaterials handling volume.3. In order to solve the job shop scheduling problem, focus on the theories andmethods of genetic algorithm researching, considering the characteristics of part plantin the magnet production system, an operation-based code was applied and the designframework is gave. Using Em-Plant software to build the simulation model and thenoptimizing the model in order to get the shortest completion time. This schedulingstrategy helps the parts department to reduce the cycle time and ensure the afteroperations.4. Establishing assembly operations’ simulation model, and finding that theproductivity of the assembly operations are too low. In order to solve this problem,considering the number of carrier, simulating and optimizing the model, then gainsolutions which can help us to decide the different numbers of the carrier in different conditions. The optimization improves the production efficiency obviously.