Titlebook: Equipment Selection for Mining: With Case Studies; Christina N. Burt,Louis Caccetta Book 2018 Springer International Publishing AG 2018 Tr

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Using Stock Market Data in Python,d. In a surface mine, equipment is often not utilised to full capacity and not accounting for this difference may lead to inferior solutions. Generally, the cost of operating equipment depends on the age of that equipment, while the decision to use a piece of equipment or not is based on the cost. T

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Equipment Selection for Mining: With Case Studies978-3-319-76255-5Series ISSN 2198-4182 Series E-ISSN 2198-4190

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Match Factor Extensionselected fleet. Prior to our work the match factor ratio has been restricted to homogeneous fleets. However, heterogeneous fleets are very common in large scale mines. In this chapter, we present a detailed account of the match factor ratio in mining and develop several extensions to the match factor ratio to allow for heterogeneous fleets.

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Future Research Directionsas been motivated and illustrated by real case studies provided by our industry collaborators. This final chapter discusses a number of open problems that provide important challenges to the research community.

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Definitions and Results from Bers’ Theoryugh small and simple has interesting variation in truck cycle times. It represents a mine in the planning stage with pre-existing equipment. Our second case study, is from an ongoing mining operation and has pre-existing equipment and includes stockpiles. This data was provided by an in-house equipment selection expert from an industry partner.

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Case Studiesugh small and simple has interesting variation in truck cycle times. It represents a mine in the planning stage with pre-existing equipment. Our second case study, is from an ongoing mining operation and has pre-existing equipment and includes stockpiles. This data was provided by an in-house equipment selection expert from an industry partner.

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2198-4182 of trucks and loaders.Presents the truck and loader equipmenThis 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 info