TY - BOOK ID - 32841995 TI - Equipment Selection for Mining: With Case Studies AU - Burt, Christina N. AU - Caccetta, Louis. PY - 2018 SN - 3319762559 3319762540 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Strip mining KW - Equipment and supplies. KW - Engineering. KW - Artificial intelligence. KW - Computational intelligence. KW - Transportation engineering. KW - Traffic engineering. KW - Transportation Technology and Traffic Engineering. KW - Computational Intelligence. KW - Artificial Intelligence (incl. Robotics). KW - Engineering, Traffic KW - Road traffic KW - Street traffic KW - Traffic, City KW - Traffic control KW - Traffic regulation KW - Urban traffic KW - Highway engineering KW - Transportation engineering KW - Civil engineering KW - Engineering KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Construction KW - Industrial arts KW - Technology KW - Open-cast mining KW - Open-cut mining KW - Open-pit mining KW - Surface mining KW - Mining engineering KW - Traffic Engineering. KW - Artificial Intelligence. UR - https://www.unicat.be/uniCat?func=search&query=sysid:32841995 AB - This unique book presents innovative and state-of-the-art computational models for determining the optimal truck–loader selection and allocation strategy for use in large and complex mining operations. The authors provide comprehensive information on the methodology that has been developed over the past 50 years, from the early ad hoc spreadsheet approaches to today’s highly sophisticated and accurate mathematical-based computational models. The authors’ approach is motivated and illustrated by real case studies provided by our industry collaborators. The book is intended for a broad audience, ranging from mathematicians with an interest in industrial applications to mining engineers who wish to utilize the most accurate, efficient, versatile and robust computational models in order to refine their equipment selection and allocation strategy. As materials handling costs represent a significant component of total costs for mining operations, applying the optimization methodology developed here can substantially improve their competitiveness. ER -