Scheduling, planning and packing are ubiquitous problems that
can be found in a wide range of real-world settings. These problems
transpire in a large variety of forms, and have enormous
socio-economic impact. For many years, significant work has been
devoted to automating the processes of scheduling, planning and
packing using different kinds of methods. However, poor scaling and
the lack of flexibility of many of the conventional methods coupled
with the fact that most of the real-world problems across the
application areas of scheduling, planning and packing nowadays tend
to be of large scale, dynamic and full of complex dependencies have
made it necessary to tackle them in unconventional ways.
This volume, "Natural Intelligence for Scheduling, Planning and
Packing Problems," is a collection of numerous natural intelligence
based approaches for solving various kinds of scheduling, planning
and packing problems. It comprises 12 chapters which present many
methods that draw inspiration from nature, such as evolutionary
algorithms, neural-fuzzy system, particle swarm algorithms, ant
colony optimisation, extremal optimisation, raindrop optimisation,
and so on. Problems addressed by these chapters include freight
transportation, job shop scheduling, flowshop scheduling,
electrical load forecasting, vehicle routing, two-dimensional strip
packing, network configuration and forest planning, among others.
Along with solving these problems, the contributing authors present
a lively discussion of the various aspects of the nature-inspired
algorithms utilised, providing very useful and important new
insights into the research areas.
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