Truck-shovel fleet cycle optimisation using GPS collision avoidance system

Knights, Benjamin, Kizil, Mehmet and Seib, Warren (2012). Truck-shovel fleet cycle optimisation using GPS collision avoidance system. In: Proceedings of the 2012 Coal Operators' Conference. 2012 Coal Operators' Conference, Wollongong, Australia, (361-370). 16-17 Feb 2012.

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
Author Knights, Benjamin
Kizil, Mehmet
Seib, Warren
Title of paper Truck-shovel fleet cycle optimisation using GPS collision avoidance system
Conference name 2012 Coal Operators' Conference
Conference location Wollongong, Australia
Conference dates 16-17 Feb 2012
Proceedings title Proceedings of the 2012 Coal Operators' Conference
Journal name Proceedings of the 2012 Coal Operators' Conference
Place of Publication Wollongong, Australia
Publisher The University of Wollongong Printery
Publication Year 2012
Sub-type Fully published paper
ISBN 9781921522574
Start page 361
End page 370
Total pages 10
Collection year 2013
Language eng
Formatted Abstract/Summary
Truck-Shovel operations in surface mines involve high costs. Fleet management systems can provide a tool to improve fleet availability, utilisation and productivity, thereby reducing those costs. However, these systems are expensive to install. Stop-Watch time and motion studies provide a cheaper alternative. They can be undertaken on any segment of the haul cycle to provide accurate timing data, as well as observations on operator performance but are very time consuming and do not provide continuous monitoring of a fleet.

This paper provides an analysis of an alternative option; using a GPS collision avoidance system for truck-shovel fleet cycle optimisation. A case study was undertaken based on an operating mine in South-east Queensland using a commercially available GPS collision avoidance system. The approach was to use the GPS collision avoidance system to collect the truck positioning, speed, and timing data, which is automatically recorded as part of its normal function then to apply this information in a conventional time and motion study. This was combined with production loading data to provide some additional performance indicators. The methodology for using in a truck-shovel fleet cycle optimisation is discussed and the results from the case study are presented. Finally, the applicability of this approach is evaluated.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Google Scholar Search Google Scholar
Created: Fri, 07 Dec 2012, 12:16:06 EST by Dr Mehmet Kizil on behalf of School of Mechanical and Mining Engineering