Metaheuristics for Business Analytics

A Decision Modeling Approach

  • Abraham Duarte
  • Manuel Laguna
  • Rafael Marti

Part of the EURO Advanced Tutorials on Operational Research book series (EUROATOR)

Table of contents

  1. Front Matter
    Pages i-x
  2. Abraham Duarte, Manuel Laguna, Rafael Martí
    Pages 1-27
  3. Abraham Duarte, Manuel Laguna, Rafael Martí
    Pages 29-55
  4. Abraham Duarte, Manuel Laguna, Rafael Martí
    Pages 57-83
  5. Abraham Duarte, Manuel Laguna, Rafael Martí
    Pages 85-103
  6. Abraham Duarte, Manuel Laguna, Rafael Martí
    Pages 105-136

About this book

Introduction

This essential metaheuristics tutorial provides descriptions and practical applications in the area of business analytics. It addresses key problems in predictive and prescriptive analysis, while also illustrating how problems that arise in business analytics can be modelled and how metaheuristics can be used to find high-quality solutions. Readers will be introduced to decision-making problems for which metaheuristics offer the most effective solution technique. The book not only shows business problem modelling on a spreadsheet but also how to design and create a Visual Basic for Applications code.

Keywords

Data mining GRASP Metaheuristic solutions for business analytics Predictive analysis Prescriptive analysis Single-solution search

Authors and affiliations

  • Abraham Duarte
    • 1
  • Manuel Laguna
    • 2
  • Rafael Marti
    • 3
  1. 1.E.T.S. Ingeniería InformáticaUniversidad Rey Juan Carlos, E.T.S. Ingeniería InformáticaMadridSpain
  2. 2.Leeds School of BusinessBoulderUSA
  3. 3.Dpto. de Estadística e I. O Facultad de MatemáticasUniversidad de ValenciaBurjassot, ValenciaSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-68119-1
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Business and Management
  • Print ISBN 978-3-319-68117-7
  • Online ISBN 978-3-319-68119-1
  • Series Print ISSN 2364-687X
  • Series Online ISSN 2364-6888
  • About this book
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