A multiple objective transportation problem approach to dynamic truck dispatching in surface mines

Ali Moradi Afrapoli, Mohammad Tabesh, Hooman Askari-Nasab

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

In surface mining operations, fleet management systems seek to make optimal decisions to handle material in two steps: path production optimization and real-time truck dispatching. This paper develops a multiple objective transportation model for real-time truck dispatching. The model addresses two major drawbacks of former models. The proposed model dispatches the trucks to destinations trying to simultaneously minimize shovel idle times, truck wait times, and deviations from the path production requirements established by the production optimization stage. To evaluate the performance of the proposed model, we developed a benchmark model based on the backbone of the most widely used fleet management system in the mining industry (Modular Mining DISPATCH). Afterward, we built a discrete event simulation model of the truck and shovel operation using an iron ore mine case study, implemented both of the dispatching models, and compared the results. The implementation of the models suggests that the multiple objective model developed in this paper is able to meet the production requirements of the operation using a fleet at 85% of the size of the deterministically calculated desired fleet. In addition, the model is able to meet the full capacity of the processing plants with a fleet of 30% less trucks than the desired fleet.

Original languageEnglish
Pages (from-to)331-342
Number of pages12
JournalEuropean Journal of Operational Research
Volume276
Issue number1
DOIs
StatePublished - Jul 1 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier B.V.

Keywords

  • Fleet management system
  • Goal programming
  • Multiple objective programming
  • Simulation
  • Truck dispatching

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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