Equipment Selection for Mining: With Case Studies

  • Christina N. Burt
  • Louis Caccetta

Part of the Studies in Systems, Decision and Control book series (SSDC, volume 150)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Background and Methodology

    1. Front Matter
      Pages 1-1
    2. Christina N. Burt, Louis Caccetta
      Pages 3-9
    3. Christina N. Burt, Louis Caccetta
      Pages 11-23
    4. Christina N. Burt, Louis Caccetta
      Pages 25-51
    5. Christina N. Burt, Louis Caccetta
      Pages 53-61
  3. Optimisation Models and Case Studies

    1. Front Matter
      Pages 63-63
    2. Christina N. Burt, Louis Caccetta
      Pages 65-74
    3. Christina N. Burt, Louis Caccetta, Palitha Welgama, Leon Fouché
      Pages 75-90
    4. Christina N. Burt, Louis Caccetta, Leon Fouché, Palitha Welgama
      Pages 91-114
    5. Christina N. Burt, Louis Caccetta, Yao-ban Chan
      Pages 115-143
    6. Christina N. Burt, Yao-ban Chan
      Pages 145-152
    7. Christina N. Burt, Louis Caccetta
      Pages 153-155
  4. Christina N. Burt, Louis Caccetta
    Pages E1-E1

About this book


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


Truck Equipment Selection Surface Mining Loader Equipment Selection Mining Equipment Selection

Editors and affiliations

  • Christina N. Burt
    • 1
  • Louis Caccetta
    • 2
  1. 1.Department of Mathematics and StatisticsThe University of MelbourneParkvilleAustralia
  2. 2.Department of Mathematics and StatisticsCurtin UniversityBentleyAustralia

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-76254-8
  • Online ISBN 978-3-319-76255-5
  • Series Print ISSN 2198-4182
  • Series Online ISSN 2198-4190
  • Buy this book on publisher's site
Industry Sectors