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Year : 2023, Volume : 14, Issue : 1and2
First page : ( 45) Last page : ( 59)
Print ISSN : 0973-4317. Online ISSN : 1945-919X. Published online : 2023  16.
Article DOI : 10.5958/1945-919X.2023.00006.3

Impact of Fleet Allocation on Production Indicators in Surface Mining

Tapia Elías1,*, González Camila2,**, Mora Jaime3,, Soto Andrés4,, Cánovas Manuel5,§, Acevedo Roberto6,

1Universidad Católica del Norte, Angamos 0610, Antofagasta, Chile

2Universidad San Sebastián, Bellevista 7, Santiago, Chile

3Minera Antucoya, Av. Apoquindo 4001, Piso 18, Las Condes, Santiago, Chile

4Universidad San Sebastián, Bellevista 7, Santiago, Chile

5Universidad Católica del Norte, Angamos 0610, Antofagasta, Chile

6Universidad San Sebastián, Bellevista 7, Santiago, Chile

(*Corresponding author) E-mail: *elias.tapia@ucn.cl

**cgonzaleza0507@gmail.com

jmoraflores01@gmail.com

andres.soto@uss.cl

§manuel.canovas@ucn.cl

roberto.acevedo.llanos@gmail.cl

Online Published on 16 December, 2023.

Abstract

The current challenges in mining have prompted companies to optimize their processes and reduce uit operations and production costs. According to Mining Safety (2017), cargo and transport constitute the most influential and expensive unit operation. Therefore, fleet allocation becomes a crucial factor, as optimal allocation enables maximum control and efficiency, leading to improved production and cost reduction. This study primarily focuses on assessing the impact of fleet allocation methods on production indicators during the loading and transport stages in open-pit mining. To achieve this, we establish a case database using operational data from a major mining company in Northern Chile, considering a fleet comprising twenty-five cargo and transport teams. The study’s methodology involves collecting operational data to analyze the current mine operation and configure the necessary information for the simulation model. Finally, we generate simulations by modifying the fleet allocation method to determine the most suitable one. The results reveal that Dynamics is the optimal allocation method for the mine, producing better outcomes in all the studied indicators. It is also concluded that the choice of fleet allocation can positively impact production in the loading and transport stages, provided it is appropriate for the mine and maximizes fleet productivity.

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