Knowledge-Based Reactive Scheduling

Reactive scheduling is concerned with monitoring and controlling the execution of predictive schedules and making any changes required to bring them in line with unanticipated events or disturbances happening in real time. Whilst predictive scheduling has been a focus of research for many years, it is only recently that explicit attention has been given to the specific problems of reactive scheduling and the relationship between the two. This publication explores the scope and status of both existing, and potential approaches to reactive scheduling. Concentration is centred on knowledge-based approaches as the importance of domain knowledge, whether stored in computers or in the brains of experienced schedulers, is becoming increasingly apparent. Contributions examine a variety of approaches ranging from reactive scheduling decision-support frameworks in which the knowledge comes mainly from the human, through to completely new approaches to automated reactive scheduling such as neural networks. The book should be of particular interest to researchers, students and engineers involved in the manufacturing and software development industries.
In addition to providing a significant reference volume, it also aims to stimulate increased discussion in this emerging field.
 
Reactive scheduling is concerned with monitoring and controlling the execution of predictive schedules and making any changes required to bring them in line with unanticipated events or disturbances happening in real time. Whilst predictive scheduling has been a focus of research for many years, it is only recently that explicit attention has been given to the specific problems of reactive scheduling and the relationship between the two. This publication explores the scope and status of both existing, and potential approaches to reactive scheduling. Concentration is centred on knowledge-based approaches as the importance of domain knowledge, whether stored in computers or in the brains of experienced schedulers, is becoming increasingly apparent. Contributions examine a variety of approaches ranging from reactive scheduling decision-support frameworks in which the knowledge comes mainly from the human, through to completely new approaches to automated reactive scheduling such as neural networks. The book should be of particular interest to researchers, students and engineers involved in the manufacturing and software development industries.
In addition to providing a significant reference volume, it also aims to stimulate increased discussion in this emerging field.