PRODUCTION SCHEDULING OPTIMIZATION WITH BINARY INTEGER PROGRAMMING MODEL: A CASE STUDY OF UNDERGROUND GOLD MINING USING SUBLEVEL STOPING METHOD

• Main Author: Dovena, Adela
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/80412
• Institution: Institut Teknologi Bandung

Sublevel stopping is one of the most widely used large-scale underground mining methods. The criteria for implementing this method are that the openings created during extraction must remain open. After extraction, the openings can be backfilled or left open, and the pillars left between the stopes can be extracted or left in place (Bullock, 2011). In practice, production scheduling is often conducted without considering stopes with the next highest cash flow available. Therefore, optimization of production scheduling is necessary using the binary integer programming (BIP) method with the goal of maximizing profit. This study compares production scheduling in a case study of underground gold mining using the sublevel stoping with delayed backfill method for both the base case scenario and the optimization scenario. The base case is conducted based on production scheduling simulation in Excel, with mining direction parallel to the ore body's strike. The optimization scenario involves production scheduling with the BIP model. BIP optimization, based on the Basiri model (2018), is applied to the mining design with non-uniform stope dimensions and varying tonnages at each level. Production scheduling for the base case and optimization scenarios is conducted for 4.5 million tons of ore with an average grade of 3.60 g/t Au over 8 years of production. The production scheduling in the optimization scenario can increase EBITDA by 3% or US$ 7M based on the present value. In the optimization scenario, the last negative EBITDA is obtained before stope production or in the 3rd period. In contrast, in the base case scenario, negative EBITDA is still obtained in the 4th year. The economic analysis results for the base case scenario show an NPV @8% of US$ 136M and an IRR of 42%. The payback period (PBP) will be achieved in 6.5 years. Meanwhile, the optimization scenario can increase NPV by about 4% from the base case or US$ 5M, with NPV @8% of US$ 141M and an IRR of 44%. The PBP will be achieved in 6.3 years. Positive cumulative cash flow in both scenarios is obtained in the 6th year when the processing plant can operate at maximum capacity.