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Table of contents

  1. Front Matter
    Pages i-xi
  2. Basic Concepts

    1. Front Matter
      Pages 1-1
    2. Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
      Pages 3-12
    3. Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
      Pages 13-18
    4. Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
      Pages 19-31
    5. Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
      Pages 33-42
  3. Theory and Algorithms

    1. Front Matter
      Pages 43-43
    2. Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
      Pages 45-56
    3. Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
      Pages 57-95
    4. Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
      Pages 97-120
    5. Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
      Pages 121-135
  4. Applications

    1. Front Matter
      Pages 137-138
    2. Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
      Pages 139-145
    3. Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
      Pages 147-178
  5. Back Matter
    Pages 179-192

About this book

Introduction

Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management. 

Keywords

Branch-and-Bound approach Lipschitz optimization applications in engineering non-convex multi-objective optimization randomized algorithms software and applications Scalarization Tchebycheff Method Pareto Sets Normal Boundary Intersection Statistical Models for Global Optimization Optimal Algorithms for Lipschitz Functions Optimal Passive Algorithm Optimal Sequential Algorithm Multidimensional Bi-Objective Lipschitz Optimization Pareto Frontier Trisection of a Hyper-rectangle Pareto Optimal Decisions Binary-Linear Model continuous problems

Authors and affiliations

  • Panos M. Pardalos
    • 1
  • Antanas Žilinskas
    • 2
  • Julius Žilinskas
    • 3
  1. 1.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  2. 2.Institute of Mathematics & InformaticsVilnius UniversityVilniusLithuania
  3. 3.Institute of Mathematics & InformaticsVilnius UniversityVilniusLithuania

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-61007-8
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-61005-4
  • Online ISBN 978-3-319-61007-8
  • Series Print ISSN 1931-6828
  • Series Online ISSN 1931-6836
  • Buy this book on publisher's site
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