Title: New MIP model for short-term planning in open-pit mines considering loading machine performance: a case study in Iran

Authors: Mohammad Mirzehi; Mojtaba Rezakhah; Amin Mousavi; Zohreh Nabavi

Addresses: Department of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Jalal AleAhmad, Nasr, Tehran, Iran ' Department of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Jalal AleAhmad, Nasr, Tehran, Iran ' Department of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Jalal AleAhmad, Nasr, Tehran, Iran ' Department of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Jalal AleAhmad, Nasr, Tehran, Iran

Abstract: Effective short-term planning in open pit mines relies on factors like equipment performance, crucial for meeting predetermined goals. Improper planning can lead to delays, necessitating a proactive approach to predict and minimise production deviations. This paper introduces a mathematical mixed integer programming (MIP) model, factoring in uncertainties related to machine availability, a key metric reflecting the quality of machine performance. Implemented in an open-pit iron mine in Iran, the model employs a stochastic approach, optimising 30 times with different random seeds for availability distributions. Results indicate improved compliance with production goals, delivering ore of superior quantity and quality for processing. A 4% enhancement in ore attainment is observed when comparing stochastic and deterministic approaches to availability parameters, underscoring the impact on block sequencing and allocation to loading machines. This study highlights the model's significance in enhancing short-term planning and mitigating uncertainties in open-pit mining operations.

Keywords: short-term mine planning; optimisation; availability; equipment performance; mixed integer programming; MIP; Iran.

DOI: 10.1504/IJMME.2023.137375

International Journal of Mining and Mineral Engineering, 2023 Vol.14 No.4, pp.341 - 364

Received: 17 May 2023
Accepted: 28 Nov 2023
Published online: 14 Mar 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article