Particle swarm optimization for scheduling problems

Metaheuristics are a class of algorithms designed to tackle hard combinatorial optimization problems where classical heuristics have failed to be effective and efficient. In principle they try to combine different concepts derived from classical heuristics, artificial intelligence, biological evolution, neural systems, and statistical mechanics in higher level frameworks with the aim of exploring the search space more effectively. This book is dedicated to a class of metaheuristics called particle swarm optimization. Particle swarm optimization was originally proposed by Kennedy as a simulation of social behavior, and it was initially introduced as an optimization method for non-linear continuous functions. Particle swarm optimization is related to artificial intelligence, and specifically to swarming theories, simulation of social behaviors, and to evolutionary computation, especially genetic algorithms, evolution strategies, and evolutionary programming. Particle swarm optimization has proved to be useful in solving a plethora of problems in science and engineering. The research presented in this book aims at adapting particle swarm optimization to combinatorial optimization problems. Based on own experiments and approaches proposed in the literature Jens Czogalla develops a general framework which incorporates particle swarm essentials and is applicable to (all) combinatorial optimization problems. The framework is validated by extensive computational experiments on two well-known scheduling problems, namely the no-wait flowshop scheduling problem and the resourceconstrained project scheduling problem. Furthermore, the framework is used to develop a search methodology for an sophisticated decision support system. The book is directed towards researchers as well as practitioners in the field.