Abstract

Off-The-Road (OTR) tires represent an important part of the operational costs of the mining industry. Each year, a typical operation consumes hundreds of tires. In general worn and damaged tires are not reused and represent a major issue for mining sustainability. In terms of operational costs, tires are only second to fuel, translating into several USD millions per year for an average mine operation. In addition, tires affect equipment performance and availability and, consequently, put at risk the capacity of the haul fleets to deliver the production targets. OTR tire lifespan depends on proper type selection. Each tire-type implies choosing a combination of rubber compounds and geometric specifications that are suited to road parameters. Medium and long term mining plans specify routes and production goals. In general, each route has a specific optimal tire type. The traditional approach is to consider the most demanding conditions and selecting a single tire type for the whole fleet. In such a way, truck dispatch is flexible as any truck can haul on any route. A drawback is that this one-size-fits-all policy increases tire consumption as the worst-case route sets the type of tire for the entire fleet. The above builds an interesting case for optimizing tire selection and haul fleet usage schedule as both decisions can be relevant in search for reducing tire consumption, decrease operational costs, and assure production plan adherence. As tire type may influence haul cycle times, assignment of trucks to different routes should also be considered. This work introduces a novel methodology for setting a usage allocation plan for the haul fleet and selecting route-oriented tire types. We test our methodology using a medium size open-pit operation in northern Chile. The case study shows a tire consumption reduction of 7.3% with respect to the traditional approach over a 5 year time span. Net present tire costs are reduced by USD 2.7 millions (-7.2%). Our methodology presents a novel approach to both reducing costs and achieving long-term production plans. (C) 2019 Elsevier Ltd. All rights reserved.