Equipment Selection for Mining: With Case Studies [electronic resource] /
Burt, Christina N.editor.
Caccetta, Louis.editor.
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.
Introduction -- Literature Review -- Match Factor Extensions -- Case Studies -- Single Location Equipment Selection -- Multiple Locations Equipment Selection -- Utilisation-based Equipment Selection -- Accurate Costing of Mining Equipment -- Future Research Directions.
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.
Transportation engineering.
Traffic engineering.
Computational intelligence.
Artificial intelligence.
Transportation Technology and Traffic Engineering.
Computational Intelligence.
Artificial Intelligence.