Haul fleet size determination is a critical task in any surface mining operation where the material is handled using the truck-and-shovel system. Although the problem of finding the optimum haulage fleet size has been thoroughly studied, there are two important shortcomings: disregarding the effects of downstream processes on the operation and ignoring the fleet management system effects. This paper presents an integrated simulation-optimization framework to address the haul fleet size determination problem surface mines and target the two shortcomings listed above. In the developed framework, the mining operation, the processing plants, and the operational decision tools communicate with each other to find the best size of the haul fleet required to meet the production schedule. Results of the study show that the developed framework is capable of handling the operation with 13% less number of trucks than the required number of trucks suggested by deterministic calculations.
AtaeepourN, BaafiEY.1999. ARENA simulation model for truck-shovel operation in despatching and non-despatching modes. Int J Surf Min, Reclam Environ. [Internet]. 13:125–129. http://doi.org/10.1080/09208119908944228
4.
BandopadhyayS.1987. Partial ranking of primary stripping equipment in surface mine planning. Int J Surf Min Reclam Environ[Internet]. 1:55–59. https://doi.org/10.1080/09208118708944102
5.
BandopadhyayS, VenkatasubramanianP.1987. Expert systems as decision aid in surface mine equipment selection. Int J Surf Min Reclam Environ[Internet].1:159–165. https://doi.org/10.1080/09208118708944114
BozorgebrahimiE, HallRA, BlackwellGH.2003. Sizing equipment for open pit mining – a review of critical parameters. Min Technol[Internet].112:171–179. http://doi.org/10.1179/037178403225003591
8.
BurtCN, CaccettaL.2007. Match factor for heterogeneous truck and loader fleets. Int J Min Reclam Environ. 21:262–270. doi: 10.1080/17480930701388606
9.
BurtCN, CaccettaL.2014. Equipment selection for surface mining: a review. Interfaces (Providence)[Internet].44:143–162. https://doi.org/10.1287/inte.2013.0732
10.
BurtC, PuchingerJ.2005. Models and algorithms for intermodal transportation and equipment selection. In: ZergerA, ArgentR, editors. MODSIM 2005 Int congr model simul[Internet]. [place unknown]: Modelling and Simulation Society of Australia and New Zealand; p. 305–310. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.375.8249
DarlingP.2011. SME mining engineering handbook. Englewood, Co: Society for Mining, Metallurgy & Exploration.
14.
DindarlooSr, OsanlooM, FrimpongS.2015. A stochastic simulation framework for truck and shovel selection and sizing in open pit mines. South Afr Inst Min Metall. 115:209–219. doi: 10.17159/2411-9717/2015/v115n3a6
15.
DouglasJ.1964. Prediction of shovel truck production: a reconciliation of computer and conventional estimates[Internet]. Stanford: Distributed by the Construction Institute. https://books.google.ca/books?id=5KM0OwAACAAJ
16.
EdwardsDJ, MalekzadehH, YisaSB.2001. A linear programming decision tool for selecting the optimum excavator. Struct Surv[Internet]. 19:113–120. https://doi.org/10.1108/EUM0000000005628
17.
ErcelebiSG, BascetinA.2009. Optimization of shovel-truck system for surface mining. J South Afr Inst Min Metall. 109:433–439.
18.
GrodzevichO, RomankoO.2006. Normalization and other topics in multi-objective optimization. In: AruliahDA, LewisGM, editors. Fields–MITACS Ind probl work. Toronto, ON: MITACS; p. 89–101.
19.
IBM. 2016. IBM ILOG CPLEX Optimization Studio.
20.
JangH, TopalE.2014. A review of soft computing technology applications in several mining problems. Appl Soft Comput. 22:638–651. doi: 10.1016/j.asoc.2014.05.019
21.
KolonjaB, MutmanskyJM.1993. Analysis of Truck Dispatching Strategies For Surface Mining Operations Using SIMAN.SME, editor. SME Annu Meet. 296:1845–1851.
22.
KomljenovicD, NourGA, PopovicN.2015. An approach for strategic planning and asset management in the mining industry in the context of business and operational complexity. Int J Min Miner Eng. [Internet]. 6:338. http://www.inderscience.com/link.php?id=73047
23.
KrauseA, MusingwiniC.2007. Modelling open pit shovel-truck systems using the machine repair model. J South African Inst Min Metall. 107:469–476.
24.
LiX, SongX.2009. Application of Genetic Algorithm to Optimize the Equipment of Coal Mine. In: 2009 Int Conf Comput Intell Softw Eng. [place unknown]; p. 1–3.
SamantaB, SarkarB, MukherjeeSK.2002. Selection of opencast mining equipment by a multi-criteria decision-making process. Min Technol.111:136–142. doi: 10.1179/mnt.2002.111.2.136
36.
SmithSD, WoodGS, GouldM, SmithSD, WoodGS, GouldM, SmithSD, WoodGS, GouldM.2000. A new earthworks estimating methodology a new earthworks estimating methodology. Constr Manag Econ.18:219–228. doi: 10.1080/014461900370843
WhiteJW, OlsonJP.1986. Computer-based dispatching in mines with concurrent operating objectives. Min Eng Littlet. 38:1045–1054.
39.
YuriyG, VayenasN.2008. Discrete-event simulation of mine equipment systems combined with a reliability assessment model based on genetic algorithms. Int J Min Reclam Environ. [Internet]. 22:70–83. http://doi.org/10.1080/17480930701589674
40.
ZengW, BaafiE, WalkerD.2017. A simulation model to study bunching effect of a truck-shovel system. Int J Min Reclam Environ. [Internet].:1–16. http://doi.org/10.1080/17480930.2017.1348284
