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

Chao Meng, Sai Srinivas Nageshwaraniyer, Amir Maghsoudi, Young Jun Son, Sean Dessureault

Research output: Contribution to journalArticle

25 Scopus citations

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® 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.

Original languageEnglish (US)
Pages (from-to)766-779
Number of pages14
JournalComputers and Industrial Engineering
Volume64
Issue number3
DOIs
StatePublished - Feb 11 2013

Keywords

  • Coal mining
  • Data-driven modeling
  • Decision making
  • Material handling system

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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