© 2017

Grouping Genetic Algorithms

Advances and Applications


  • Treats problems from a spectrum of industrial disciplines as easy-to-understand and solve grouping structures

  • Schematics, flow charts and algorithmic descriptions render the content easy to digest

  • Shows the reader new efficient heuristic grouping techniques

  • Illustrative computational examples demonstrate the effectiveness of the algorithm, even in a fuzzy problem environment


Part of the Studies in Computational Intelligence book series (SCI, volume 666)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Michael Mutingi, Charles Mbohwa
      Pages 3-29
    3. Michael Mutingi, Charles Mbohwa
      Pages 31-42
  3. Grouping Genetic Algorithms

    1. Front Matter
      Pages 43-43
  4. Research Applications

  5. Conclusions and Extensions

    1. Front Matter
      Pages 229-229
    2. Michael Mutingi, Charles Mbohwa
      Pages 231-238
  6. Back Matter
    Pages 239-243

About this book


This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment. A wide range of industrial grouping problems, drawn from diverse fields such as logistics, supply chain management, project management, manufacturing systems, engineering design and healthcare, are presented. Typical complex industrial grouping problems, with multiple decision criteria and constraints, are clearly described using illustrative diagrams and formulations. The problems are mapped into a common group structure that can conveniently be used as an input scheme to specific variants of grouping genetic algorithms. Unique heuristic grouping techniques are developed to handle grouping problems efficiently and effectively. Illustrative examples and computational results are presented in tables and graphs to demonstrate the efficiency and effectiveness of the algorithms.

Researchers, decision analysts, software developers, and graduate students from various disciplines will find this in-depth reader-friendly exposition of advances and applications of grouping genetic algorithms an interesting, informative and valuable resource.


Genetic Algorithms Fuzzy Multi-criterion Optimization Multi-criterion Decision Making Metaheuristics Assembly-line Balancing Task Assignment in Healthcare Cell Manufacturing

Authors and affiliations

  1. 1.Department of Industrial EngineeringNamibia University of Science and TechnologyWindhoekNamibia
  2. 2.Faculty of Engineering and Built Envmt.University of JohannesburgJohannesburgSouth Africa

About the authors

Michael Mutingi is a Lecturer and a Researcher in Industrial and Systems Engineering. He researches in healthcare operations management, biologically inspired metaheuristic optimization, fuzzy multi-criteria decision methods, and lean healthcare. Other areas of interest include green supply chain management, logistics management, manufacturing systems simulation, and business system dynamics.

Charles Mbohwa is an established researcher and professor in operations management, manufacturing systems, green supply chain management and sustainability engineering, optimization, and  his specializations include renewable energy systems, and bio-fuel feasibility.

Bibliographic information

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