Underground Truck Dispatching Using Real-Time Data Analytics
Sihong Peng, Vale; Greg Yuriy, Vale
While truck dispatching is prevalent at surface mines, no GPS coverage and limited network availability has presented some unique challenges facing underground fleet management. With digital infrastructure paving the way for Industry 4.0, Vale has seized the opportunity to develop a dispatch program that will utilize real-time data analytics to enable real-time dynamic fleet allocation. The core process encompasses an iterative process of collecting data, learning decision making processes of the current operations, developing dispatching algorithms, testing the program, and evaluating system performance. Through this process Vale will be able to deliver more predictable performance, enhance operational safety, and integrate other business units with operations to enable advanced utilization of data analytics.
dispatching, underground mining, Vale, machine learning, Industry 4.0, dynamic allocation