Title: A stochastic integrated simulation and mixed integer linear programming optimisation framework for truck dispatching problem in surface mines

Authors: Ali Moradi-Afrapoli; Hooman Askari-Nasab

Addresses: Mining Optimization Laboratory (MOL), Department of Civil and Environmental Engineering, School of Mining and Petroleum Engineering, University of Alberta, 3-133 Markin/CNRL Natural Resources Engineering Facility, Edmonton, Alberta, T6G 2W2, Canada ' Mining Optimization Laboratory (MOL), Department of Civil and Environmental Engineering, School of Mining and Petroleum Engineering, University of Alberta, 3-133 Markin/CNRL Natural Resources Engineering Facility, Edmonton, Alberta, T6G 2W2, Canada

Abstract: Making near optimal and close to reality decisions on the destination of trucks is vital for maximising the utilisation of truck and shovel fleets and subsequently minimising the operating costs in surface mines. We developed an integrated simulation and optimisation framework for solving truck dispatching problems in surface mines. The developed framework uses simulation modelling to imitate mining operations and capture technical uncertainties. It also applies uncertainty-based mixed integer linear optimisation modelling to dispatch trucks while capturing practical uncertainties. The developed optimisation model simultaneously optimises truck fleet utilisation, shovel fleet utilisation, and plant feed rate. The model considers the stochastic nature of the dispatching parameters and includes travel time uncertainties in the decision-making procedure. A comparison between the application of the developed optimisation model with a currently in the market optimisation model using the developed integrated simulation and optimisation framework showed 11% improvement in the production of the case study.

Keywords: truck dispatching; surface mining; fleet management system; stochastic optimisation.

DOI: 10.1504/IJMME.2020.111929

International Journal of Mining and Mineral Engineering, 2020 Vol.11 No.4, pp.257 - 284

Accepted: 05 Jun 2020
Published online: 16 Dec 2020 *

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