Genetic algorithms were the first evolutionary algorithm designed. These algorithms simulate natural selection to produce good solutions quickly for complex problems. Job shop scheduling with sequence dependent setup times is an NP-hard problem – any algorithm to optimize this problem has an...

Genetic Algorithm (GA) is one of the most popular optimization solutions. It has been implemented in various applications such as scheduling. Badly designed timetables are not just inconvenient but proved expensive in terms of wasting time and money. The flows of GA are using selection,...

We discuss the performance of scheduling algorithms for a certain kind of manufacturing environment, called the "Flexible Flowshop", which consists of a certain number of machine centers. Each center has one or more identical parallel machines. Each part is processed by at most one machine in...

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems. It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important...