For many years, significant work has been devoted to automating the processes of scheduling, planning and packing using different kinds of methods.
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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.
The LNCS journal Transactions on Computational Science reflects recent developments in the field of Computational Science, conceiving the field not as a mere ancillary science but rather as an innovative approach supporting many other scientific d. This edition was.
Do you like to build things? Are you ever frustrated at having to compromise your designs to fit whatever parts happen to be available?
Would you like to fabricate your own parts? The development of new-generation micro-manufacturing technologies and systems has revolutionised the way products are designed and manufactured today with a s- nificant impact in a number of key industrial sectors. Micro-manufacturing techno- gies a.