Grouping Genetic Algorithms: Advances And Applications (studies In Computational Intelligence)
by Michael Mutingi /
2016 / English / PDF
6.2 MB Download
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.
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.
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.