Production Scheduling Method based on Identification Parsing and its Application

Abstract

This paper introduces a method for generating production scheduling plans based on identifier resolution, along with related equipment. The method includes several key steps: Firstly, it acquires attribute data for multiple processing nodes, which encompasses information
such as the rated processing time, rated yield rate, and current operational status. Secondly, it retrieves multiple historical scheduling plans
along with the historical attribute data for each processing node involved in those plans. Then, based on the historical attribute data, it determines the stability of each processing node, reflecting the consistency of the nodes performance across different historical scheduling plans.
Following this, it calculates the correlation coefficients between processing nodes according to their stability and historical attribute data,
quantifying the degree of mutual influence between adjacent nodes. Subsequently, it constructs multiple directed graphs using the correlation
coefficients and current attribute data, with each graph representing a set of scheduling plans. Within these graphs, it calculates the first priority level for each scheduling plan and selects the plan with the highest first priority as the candidate scheduling plan. Lastly, it evaluates the
second priority level of each candidate scheduling plan based on attribute data and stability, choosing the candidate with the highest second
priority as the target scheduling plan. This application pertains to the field of intelligent scheduling technology, aiming to improve the accuracy and efficiency of scheduling plan formulation.