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.

Recent research in Natural Intelligence for Scheduling, Planning and Packing Problems

Inhalt
Global Optimization in Supply Chain Operations.- Solving Real-World Vehicle Routing Problems with Evolutionary Algorithms.- A Genetic Algorithm with Priority Rules for Solving Job-Shop Scheduling Problems.- An Estimation of Distribution Algorithm for Flowshop Scheduling with Limited Buffers.- Solving Hierarchically Decomposable Problems with the Evolutionary Transition Algorithm.- Electrical Load Forecasting Using a Neural-Fuzzy Approach.- Quantised Problem Spaces and the Particle Swarm Algorithm.- A Coevolutionary Particle Swarm Algorithm for Bi-Level Variational Inequalities: Applications to Competition in Highway Transportation Networks.- Ant Colony Optimization and Its Application to the Vehicle Routing Problem with Pickups and Deliveries.- Evolutionary and Ant Colony Optimization Based Approaches for a Two-Dimensional Strip Packing Problem.- Diagnosis, Configuration, Planning, and Pathfinding: Experiments in Nature-Inspired Optimization.- A Hybrid Intelligent System for Distributed Dynamic Scheduling.