Single Location Equipment Selection

Burt, Christina N.; Caccetta, Louis; Welgama, Palitha; Fouché, Leon

doi:10.1007/978-3-319-76255-5_6

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…Menurut Zeng [6], alat angkut mengalami tambahan waktu pengangkutan dikenal dengan istilah bunching time yang tidak layak untuk melewati alat angkut lainnya karena pada perusahaan PT MSP menerapkan satu jalur tambang dengan satu rute. Menurut Burt, dkk [7], bunching effect dapat terjadi ketika dua atau lebih alat angkut dengan efisiensi kerja yang berbeda-beda bergerak dalam satu rute yang sama.…”

Section: A Pendahuluanunclassified

Optimasi Biaya Produksi Overburden dan Kaksa Menggunakan Program Linear dan Bunching Effect

Tampubolon

2023

JRTP

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Abstract. Problems at the research location indicate that production has not been achieved, causing high mining operational costs. The aim of this research is to increase production targets for operational costs incurred using the rupiah exchange rate with the assumption that 1$ = Rp. 14,300. The research method uses linear programming and the bunching effect. Data collection was carried by calculating the number of available mining operational tools and cycle time data is taken as much as 270 data on 3 mining fleets. The actual productivity generated is 328.49 BCM/hour with an actual production cost of $2.35 USD/BCM with a predetermined target of 500 BCM/hour. The results of the actual conditions need to be corrected by optimizing the number of conveyances to be used using the POM for Windows application, namely the number of conveyances used is 10 units with a production of 520.22 BCM/hour and a production cost of $1.6 USD/BCM. The results showed that the use of 10 units of means of transportation resulted in optimal production with minimum costs compared to the use of 9 units of means of transportation. The loss of time experienced by the transportation equipment due to the bunching effect is 7 hours/month due to differences in engine power on the transportation equipment, different operator skills, undulations street and puddles on the road surface, non-ideal mine road geometry. Abstrak. Permasalahan di lokasi penelitian menunjukkan belum tercapainya produksi, sehingga menyebabkan tingginya biaya operasional penambangan. Tujuan dilakukan penelitian guna meningkatkan target produksi terhadap biaya operasional yang dikeluarkan menggunakan nilai tukar rupiah dengan asumsi 1$ = Rp 14.300. Metode penelitian menggunakan program linear dan bunching effect. Pengambilan data dilakukan dengan menghitung jumlah alat operasional penambangan yang tersedia serta data cycle time diambil sebanyak 270 data pada 3 fleet penambangan. Produktivitas aktual yang dihasilkan sebesar 328,49 BCM/jam dengan biaya produksi aktual sebesar $2,35 USD/BCM dengan target yang sudah ditetapkan sebesar 500 BCM/jam. Hasil kondisi aktual perlu dilakukan tindakan perbaikan dengan melakukan optimasi jumlah alat angkut yang akan dipakai menggunakan aplikasi POM for Windows yaitu jumlah alat angkut yang dipakai sebanyak 10 unit dengan produksi sebesar 520,22 BCM/jam dan biaya produksi sebesar $1,6 USD/BCM. Hasil penelitian menunjukkan bahwa pemakaian 10 unit alat angkut menghasilkan produksi yang optimal dengan biaya yang minimum dibandingkan dengan pemakaian 9 unit alat angkut. Hilangnya waktu yang dialami alat angkut akibat adanya bunching effect sebesar 7 jam/bulan karena terdapat perbedaan daya mesin pada alat angkut, keterampilan operator yang berbeda-beda, terdapatnya undulasi jalan, genangan air pada permukaan jalan dan geometri jalan tambang yang tidak ideal.

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Section: A Pendahuluanunclassified

Optimasi Biaya Produksi Overburden dan Kaksa Menggunakan Program Linear dan Bunching Effect

Tampubolon

2023

JRTP

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“…Krause (2007) estimate the truck fleet size using Winston (Machine Repair), FPC, Talpac, and Elbrond models in open-pit truck-shovel systems. Burt et al (2005) provide a linear programming model for the equipment selection problem in surface mining systems. Their model aims to minimize the operating cost of trucks and shovels while restricted by some constraints.…”

Section: Equipment Selection and Sizing Problem In Open-pit Miningmentioning

confidence: 99%

Truck fleet size selection in open-pit mines based on the match factor using a MINLP model

et al. 2021

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The present study aims to propose new strategies based on mixed-integer non-linear programming (MINLP) models for the equipment sizing (ES) problem to verify the overall efficiency of the fleet. The developed models estimate the optimal size of trucks concerning the match factor value with two different strategies. The first strategy deals with each loader type, and the second one is applied simultaneously with all types of loaders. The proposed approaches are compared to a simulation strategy to assess the models. Implementing models with a copper mine case study provides a more efficient haul fleet size than the decisions offered by the simulation method. Moreover, the presented strategies provide an effective way to improve equipment performance where the current mine strategy does not adapt well. A key contribution of this research is the development, implementation, and verification of new optimization and simulation methods to address the ES problem in open-pit mines.

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Single Location Equipment Selection

Cited by 2 publications

References 1 publication

Optimasi Biaya Produksi Overburden dan Kaksa Menggunakan Program Linear dan Bunching Effect

Optimasi Biaya Produksi Overburden dan Kaksa Menggunakan Program Linear dan Bunching Effect

Truck fleet size selection in open-pit mines based on the match factor using a MINLP model

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