Dynamic weighted heuristic scheduling method of automatic manufacturing system

Abstract

The invention discloses a dynamic weighting heuristic scheduling method for an automatic manufacturing system. The method comprises the following steps: modeling the automatic manufacturing system byadopting a Petri network; reading a data value corresponding to each library in the Petri net model, and solving an incidence matrix of the libraries and the changes in the Petri net model; based on the incidence matrix and the heuristic A * search algorithm, the scheduling scheme of the system from the initial state node to the target node can be searched in a shorter time without predicting thedepth of the scheduling scheme, and the quality of the scheduling scheme does not exceed a preset range. An automatic manufacturing system is used as an object; a dynamic weighting algorithm in the reachable graph is adopted; according to the method, the nodes are evaluated by adding extra weights to the heuristic function to find out the most satisfactory path, the speed of finding the optimal path can be remarkably increased under the condition of sacrificing a small amount of scheduling result quality, the efficiency in practical application is effectively improved, and the method does notneed to predict the depth of a system scheduling scheme in advance.

Description

technical field [0001] The invention relates to the field of automatic manufacturing systems, in particular to a dynamic weighted heuristic scheduling method for automatic manufacturing systems. Background technique [0002] Automated manufacturing systems are computer-controlled systems composed of limited resources that can handle different types of components. In order to effectively operate an automated manufacturing system and make full use of system resources, it is necessary to coordinate and control the use of shared resources. Automatic manufacturing systems include many types of systems, such as workshop manufacturing systems, flexible manufacturing systems, and so on. In an automated manufacturing system, available resources (such as machines, robots, drivers, programs, etc.) can be shared among processes running simultaneously (such as components, vehicles, data, etc.), and they must compete for resource allocation and Achieve some system goals, such as maximiz...

AI Technical Summary

Problems solved by technology

However, the DWA algorithm needs to estimate the depth of the final solution in advance, which is not suitable for automatic manufacturing systems with alternative routes

In the actual automatic manufacturing system, it is very common that the depth cannot be predicted in advance, and the existing methods cannot effectively solve this problem

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