Overview
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 666)
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Table of contents (13 chapters)
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Front Matter
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Introduction
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Front Matter
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Grouping Genetic Algorithms
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Front Matter
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Research Applications
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Front Matter
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Conclusions and Extensions
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Front Matter
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Back Matter
Keywords
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.
Authors and Affiliations
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
Book Title: Grouping Genetic Algorithms
Book Subtitle: Advances and Applications
Authors: Michael Mutingi, Charles Mbohwa
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-44394-2
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2017
Hardcover ISBN: 978-3-319-44393-5Published: 12 October 2016
Softcover ISBN: 978-3-319-83048-3Published: 16 June 2018
eBook ISBN: 978-3-319-44394-2Published: 04 October 2016
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIV, 243
Number of Illustrations: 78 b/w illustrations
Topics: Computational Intelligence, Operations Research/Decision Theory, Artificial Intelligence, Industrial and Production Engineering, Operations Research, Management Science
Industry Sectors: Aerospace, Automotive, Biotechnology, Chemical Manufacturing, Consumer Packaged Goods, Electronics, Energy, Utilities & Environment, Engineering, Finance, Business & Banking, Health & Hospitals, IT & Software, Law, Materials & Steel, Oil, Gas & Geosciences, Pharma, Telecommunications