Data-driven modeling and simulation framework for material handling systems in coal mines

  • Authors:

  • Chao Meng;Sai Srinivas Nageshwaraniyer;Amir Maghsoudi;Young-Jun Son;Sean Dessureault

  • Affiliations:

  • Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721, USA;Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721, USA;Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721, USA;Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721, USA;Mining and Geological Engineering, The University of Arizona, Tucson, AZ 85721, USA

  • Venue:
  • Computers and Industrial Engineering
  • Year:
  • 2013

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Abstract

In coal mining industry, discrete-event simulation has been widely used to support decisions in material handling system (MHS) to achieve premiums on revenues. However, the conventional simulation modeling approach requires extensive expertise of simulation during the modeling phase and lacks flexibility when the MHS structure changes. In this paper, a data-driven modeling and simulation framework is developed for MHS of coal mines to automatically generate a discrete-event simulation model based on current MHS structural and operational data. To this end, a formal information model based on Unified Modeling Language (UML) is first developed to provide MHS structural information for simulation model generation, production information for simulation execution, and output requirement information for defining simulation outputs. Then, Petri net-based model generation procedures are designed and used to automatically generate a simulation model in Arena(R) based on the simulation inputs conforming to the constructed information model. The proposed framework is demonstrated for one of the largest open-pit coal mines in the USA, and it has been demonstrated that the framework can be used to effectively generate the simulation models that precisely represent MHS of coal mines, and then be used to support various decisions in coal mining such as equipment scheduling.