© 2018

Open Problems in Optimization and Data Analysis

  • Panos M. Pardalos
  • Athanasios Migdalas


  • Overviews open problems in optimization, computational geometry, algorithms, logistics, data science, and statistics

  • Presents theoretical and practical techniques

  • Broadens understanding and significance of challenging and open problems


Part of the Springer Optimization and Its Applications book series (SOIA, volume 141)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Chao Li, Jing Yuan, Ding-Zhu Du
    Pages 9-22
  3. George Zioutas, Chris Chatzinakos, Athanasios Migdalas
    Pages 23-47
  4. Roshanak Mohammadivojdan, Joseph Geunes
    Pages 61-82
  5. Konstantina Skouri, Angelo Sifaleras, Ioannis Konstantaras
    Pages 83-90
  6. Yannis Marinakis, Magdalene Marinaki, Athanasios Migdalas
    Pages 91-127
  7. Abdessamad Ait El Cadi, Mustapha Ratli, Nenad Mladenović
    Pages 129-149
  8. Tran Duc Quynh, Nguyen Quang Thuan
    Pages 151-170
  9. L. Mallozzi, R. Messalli, S. Patrì, A. Sacco
    Pages 171-181
  10. Leo Liberti, Carlile Lavor
    Pages 183-223
  11. Thiago Pereira, Daniel Aloise, Jack Brimberg, Nenad Mladenović
    Pages 249-270
  12. Dimitris Souravlias, Konstantinos E. Parsopoulos
    Pages 271-284
  13. Georgios K. D. Saharidis, Antonios Fragkogios
    Pages 305-317

About this book


Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book.  Each contribution provides the fundamentals  needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline.

The contributions contained in this book are based on lectures focused on “Challenges and Open Problems in Optimization and Data Science” presented at the Deucalion Summer Institute for Advanced Studies in Optimization, Mathematics, and Data Science in August 2016. 


Optimization Problems Supply and Demand Selection Problems Global Optimization Distance Geometry Vehicle Routing Problem Open problems quantitative logistics operations research data analysis Modelling Multiprocess

Editors and affiliations

  • Panos M. Pardalos
    • 1
  • Athanasios Migdalas
    • 2
  1. 1.Industrial and Systems, Engineering DepartmentUniversity of Florida, Center For Applied OptimizationGainesvilleUSA
  2. 2.Industrial Logistics, ETS InstituteLulea University of TechnologyNorrbottenSweden

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