An asset-management oriented methodology for mine haul-fleet usage scheduling

Different complexities force mining companies to find efficient ways to respond to demand challenges and ensure long-term sustainability. It explains, in part, the increase in the use of prescriptive analytics to optimize physical-asset life-cycle costs and decrease greenhouse gas (GHG) emissions. Mining, being an asset-intensive industry, provides huge improvement opportunities. This is especially true for scheduling practices of mine haulage fleet usage in long term planning. Fleet aging implies important cost increases in maintenance and repair (M&R), and overhauls. Fleets are often heterogeneous in term of truck performance, fuel consumption and GHG emissions. Sub-optimal scheduling decisions may induce severe cost over-runs and increased emissions. This paper proposes an original mixed integer programming formulation to optimize mine haulage equipment scheduling in the long term. The model considers the effects of equipment aging, fuel consumption, payload capacity and cycle times. Our formulation handles different aspects that according to author's knowledge have not been considered in the literature as a whole: (i) joint minimization of fuel, M&R, and overhaul costs, (ii) reduction of GHG emissions, (iii) heterogeneous equipment performance metrics, (iv) increase in cycle times due to mine aging. The case study shows a cost reduction of 13% in the discounted flows associated with fuel, M&R, and overhauls in a time horizon of 10 years. This figure translates into an NPV gain of 13.1 million USD. Additionally, GHG emissions are reduced by an average of 3470 t/year or 11% overall.