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Multiobjective Scheduling by Genetic Algorithms describes methods
for developing multiobjective solutions to common production
scheduling equations modeling in the literature as flowshops, job
shops and open shops. The methodology is metaheuristic, one
inspired by how nature has evolved a multitude of coexisting
species of living beings on earth. Multiobjective flowshops, job
shops and open shops are each highly relevant models in
manufacturing, classroom scheduling or automotive assembly, yet for
want of sound methods they have remained almost untouched to date.
This text shows how methods such as Elitist Nondominated Sorting
Genetic Algorithm (ENGA) can find a bevy of Pareto optimal
solutions for them. Also it accents the value of hybridizing Gas
with both solution-generating and solution-improvement methods. It
envisions fundamental research into such methods, greatly
strengthening the growing reach of metaheuristic methods. This book
is therefore intended for students of industrial engineering,
operations research, operations management and computer science, as
well as practitioners. It may also assist in the development of
efficient shop management software tools for schedulers and
production planners who face multiple planning and operating
objectives as a matter of course.
Multiobjective Scheduling by Genetic Algorithms describes methods
for developing multiobjective solutions to common production
scheduling equations modeling in the literature as flowshops, job
shops and open shops. The methodology is metaheuristic, one
inspired by how nature has evolved a multitude of coexisting
species of living beings on earth. Multiobjective flowshops, job
shops and open shops are each highly relevant models in
manufacturing, classroom scheduling or automotive assembly, yet for
want of sound methods they have remained almost untouched to date.
This text shows how methods such as Elitist Nondominated Sorting
Genetic Algorithm (ENGA) can find a bevy of Pareto optimal
solutions for them. Also it accents the value of hybridizing Gas
with both solution-generating and solution-improvement methods. It
envisions fundamental research into such methods, greatly
strengthening the growing reach of metaheuristic methods. This book
is therefore intended for students of industrial engineering,
operations research, operations management and computer science, as
well as practitioners. It may also assist in the development of
efficient shop management software tools for schedulers and
production planners who face multiple planning and operating
objectives as a matter of course.
Relational databases have quickly come to be regarded as a natural
and efficient way of organizing information. Duplicate data can be
eliminated and powerful set-theoretic operations can be used to
manipulate data. But finding the right relations for a database is
not yet a trivial step for the uninitiated. This book presents a
comprehensive logic programming implementation of the relational
design methodology. It employs TURBO Prolog to test and establish
computational viability of the relevant algorithms. It also
presents the expert system prototype of a user interface, designed
especially for builders of computerized databases who may have no
formal training in database design.
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