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Development of a real-time fleet cost tool as part of an integrated remote mine control centre
Abstract
In large coal strip mines, there are many different processes at work: stripping, overburden removal using trucks/shovels, cast blasting, slot dozer pushing, more fragmentation and further truck/shovel benches. A key component to each of these steps is the use of machines. In modern operations, most machine costs are tracked within Enterprise systems that transact costs and repair work order management. Similarly, in contemporary mines most machines are tracked using technology that provides precise performance measures for each key process. Such technologies include productivity monitors on draglines, dozers and drills and fleet management systems for trucks and shovels. Linking these two sources of seemingly disparate data - costs and performance by machine - would allow for the precise calculation of unit costs by specific machine. This is similar to an activity-based costing system, except in this machine-based system, calculations are made at an even finer granular level. There are many uses for such an integrated information system in mine planning and performance management, both in real-time and through historical analysis. A real-time fleet costs (FLC) measurement process was developed as part of a larger blending-related, multi-million dollar project centred on a centralised remote control room developed at the University of Arizona's Mining Information Systems and Operations Management Mine Control Center. The FLC component of the overall blending system would allow a mine controller to manage the equipment fleet by viewing its cost in real time and, when mixed with other revenue-related components, the exact profit. For example, a mine controller may show high utilisation while still maintaining a production surplus. This could occur, for example, if the equipment is driven long distances. This might appear in traditional performance metrics as 'high production performance', but in reality may prove less profitable. This paper discusses the technical development of the FLC, its deployment and its impact on controller performance, and compares mine profitability before and after its application.
... The literature has presented a number of different fleet management systems, tools, and models which have been developed for a number of different decision-making situations. For example, Antuñano and Dessureault (2011) have developed a real-time fleet cost tool and Andersen et al. (2009) present an optimization model that improves the integration of vehicle management and service network design. Knowles and Baglee (2015) have proposed an asset management strategy for vehicle fleets based upon the use of preinstalled vehicle telematics systems which offer an opportunity for operators to continuously monitor the performance and effectiveness of their vehicles. ...
... Real-time accident handling (Ngai et al. 2012), Real-time bus fleet management (Hounsell et al. 2012), Dynamic fleet management problem (Shi et al. 2014) Proactive decisions Developing predictions and plans: actions before something happens History and life-cycle data, including technical and economic data Fleet-wide diagnostic and prognostic assessment , proactive monitoring (Voisin et al. 2013), Optimized resource utilization (Andersen et al. 2012; Mishra et al. 2013), Optimizing reliability, availability and maintainability of fleet, Fleet cost management (Antuñano and Dessureault 2011) Strategic decisions Long-term strategic decisions Plenty of time and consideration can be used History and life-cycle data, emphasis on economic data ...
Large amounts of data are increasingly gathered in order to support decision making processes in asset management. The challenge is how best to utilise the large amounts of fragmented and unorganised data sets to benefit decision making , also at fleet level. It is therefore important to be able to utilize and combine all the relevant data, both technical and economic, to create new business knowledge to support effective decision making especially within diverse situations. It is also important to acknowledge that different types of data are required in different decision making context. A review of the literature has shown that decision making situations are usually categorized according to the decision making levels, namely strategic, tactical and operational. In addition, they can be classified according to the amount of time used in decision making. For example, two situations can be compared: 1) optimization decision where a large amount of time and consideration is used to determine an optimum solution, and 2) decisions that need to be made instantly. Fleet management of industrial assets suffers from a lack of asset management strategies in order to ensure the correct data is collected, analysed and used to inform critical business decisions with regard to fleet management. In this paper we categorize the decision making process within certain situation and propose a new framework to identify fleet decision making situations.
Large amounts of data are increasingly gathered in order to support decision making processes in asset management. The challenge is how best to utilise the large amounts of fragmented and unorganised data sets to benefit decision making, also at fleet level. It is therefore important to be able to utilize and combine all the relevant data, both technical and economic, to create new business knowledge to support effective decision making especially within diverse situations. It is also important to acknowledge that different types of data are required in different decision making context. A review of the literature has shown that decision making situations are usually categorized according to the decision making levels, namely strategic, tactical and operational. In addition, they can be classified according to the amount of time used in decision making. For example, two situations can be compared: (1) optimization decision where a large amount of time and consideration is used to determine an optimum solution, and (2) decisions that need to be made instantly. Fleet management of industrial assets suffers from a lack of asset management strategies in order to ensure the correct data is collected, analysed and used to inform critical business decisions with regard to fleet management. In this paper we categorize the decision making process within certain situation and propose a new framework to identify fleet decision making situations.
In an effort to improve the return on investment for assets, it is becoming increasingly important for companies to develop and implement techniques for more efficient asset management, particularly through condition monitoring. Improved reliability and productivity levels for technological assets such as fleet vehicles and material handling systems in factory and warehouse environments are key metrics for competitiveness. On a system such as a forklift fleet, distributed on-demand condition monitoring, combined with model-based fault analysis and prediction techniques, can raise productivity through more efficient maintenance schedules and reduced downtime. A small, customizable embedded sensor system is described for forklifts, with wireless connectivity and automatic status reporting for a set of condition indicators. Technical specifications are based on information flow for providing asset tracking of location, hours used, and machine condition. The system will be capable of logging and transmitting sensor data over a cellular network to a centralized server and database system capable of providing improved model-based condition monitoring reports to fleet owners and operators. An embedded system has been designed, prototyped, and demonstrated in field service using cell modems and a web-based reporting tool.
This paper presents a methodology for the optimum control of shovel and truck operations in open-pit mining. Particularly discussed are the problems of material flow planning, truck dispatching, and equipment matching. The linear programming technique is used for planning material flows along paths in the haulage network, the maximum intertruck-time deviation rule of truck dispatching is proposed for facilitating implementation of automated truck dispatching systems, and the least square of intertruck-time deviation is suggested as the criterion for optimally matching trucks with shovels.