Machine learning based scheduling method for cranes in steelmaking areas
Improving the operation efficiency of cranes in the steelmaking area can reduce energy consumption for transportation while effectively linking the preceding and succeeding processes,which is of certain value for green production,cost reduction,and efficiency increase.In this regard,this article proposed a crane scheduling optimization method driven by simulation modeling and ma-chine learning.Firstly,multi-agent is used to establish a production simulation model for the steel-making area,which is driven by historical production plans and crane scheduling workflows.Subse-quently,the simulation model is run multiple times to obtain a large number of high-quality crane op-eration samples through built-in sample evaluation formulas.Finally,a random forest model is em-ployed to learn from the samples and obtain a machine learning model for matching cranes with trans-portation tasks.Experimental analysis shows that applying the machine learning model to crane sched-uling decisions can increase the proportion of effective transportation time,thereby reducing energy consumption losses caused by mismatched transportation tasks,path avoidance,etc.This advantage is particularly significant under heavy production loads.Furthermore,the crane scheduling machine learn-ing model is decoupled from the steelmaking plan,exhibiting high flexibility in practical applications.