A scheduling method for intelligent manufacturing of electric machines based on logistics simulation

Abstract

The invention provides a scheduling method for intelligent manufacturing of electric machines based on logistics simulation, The simulation optimization technology is applied, A layerer modeling is carried out on that production logistics of the intelligent manufacturing of the actual electric motor, With the help of the information trigger mechanism of the simulation system, the comprehensive heuristic optimization algorithm and the priority dispatch rule are used to control the simulation logic. The debugging results are verified by the simulation model and the scheduling plan is given to enhance the flexibility of the intelligent manufacturing assembly line of the motor, so as to cope with the highly mixed-flow production situation of multiple varieties and small batches.

Description

technical field [0001] The invention belongs to the field of intelligent motor manufacturing, and in particular relates to an intelligent motor manufacturing scheduling method based on logistics simulation. Background technique [0002] In large-scale motor manufacturing enterprises, because the production process is composed of different components through series and parallel connection, which involves many changes and uncertain factors, the production mode is often difficult to define, and it depends on the planner's long experience. In addition, more diverse customer needs will introduce more multi-variety and small-batch manufacturing orders, more insertion orders and loose orders, which will further increase the complexity of the production scheduling process. [0003] At the same time, under the balance of out-of-stock cost and inventory cost, modern manufacturing enterprises are also improving to the JIT production mode. JIT pursues to produce the required products ...

AI Technical Summary

Problems solved by technology

In the past, most of the simulation models were built without distinguishing each component module, and the same modeling method was adopted for the entire simulation process. This has little impact on the flow-through order with few varieties and large quantities, but in the face of more and more varieties and small quantities. If the order demand is high, it is impossible to achieve accurate production planning

To sum up, how to apply scientific methods to make the motor intelligent manufacturing production process modeled separately according to the process characteristics of each part and adopt an appropriate scheduling optimization algorithm and establish an information transmission mechanism to adapt to highly mixed production needs, The technology in this direction still has room for improvement and development

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