Dynamic scheduling of steel casting and milling using multi-agents

Abstract

This paper presents a case study on the use of multi-agents for integrated dynamic scheduling of steel milling and casting. Steel production is an extremely complex problem requiring the consideration of several different constraints and objectives of a range of processes in a dynamic environment. Most research in steel production scheduling considers static scheduling of processes in isolation. In contrast to earlier approaches, the multi-agent architecture proposed consists of a set of heterogeneous agents which integrate and optimize a range of scheduling objectives related to different processes of steel production, and can adapt to changes in the environment while still achieving overall system goals. Each agent embodies its own scheduling model and realizes its local predictive-reactive schedule taking into account local objectives, real-time information and information received from other agents. Agents cooperate in order to find a globally good schedule, which is able to effectively react to real-time disruptions, and to optimize the original production goals whilst minimising disruption carried by unexpected events occurring in real-time. The inter-agent cooperation is based on the Contract Net Protocol with commitment.

Keywords:

• steel production
• dynamic scheduling
• multi-agents
• tabu search
• contract net protocol

Acknowledgements

Research supported by EPSRC grant number GR/N04225/01, DASH associates and a committee of vice chancellors and Principals ORS award.

Professor Peter Cowling leads the Modelling Optimisation Scheduling and Intelligent Computing (MOSAIC) research group at the University of Bradford (http://mosaic.ac). He has worked extensively in the areas of scheduling, optimisation and modelling, both within academia, where he has published over 50 papers, and as a consultant, having led the team responsible for the development of models and heuristics for the successful SteelPlanner range of steel scheduling software, and having developed systems used for scheduling staff in one of the UK's largest financial institutions. He obtained MA and Dphil degrees from the University of Oxford. Current research interests include flexible evolutionary approaches and hyperheuristics applied to realistic, dynamic models of real-world personnel and production scheduling, and the development of Artificial Intelligence approaches for game playing agents.

Dr Djamila Ouelhadj is a Post-doctoral Research Fellow in the School of Computer Science and IT at the University of Nottingham, and a member of the Automated Scheduling Optimisation and Planning (ASAP) Research Group. She obtained her PhD from he University of Nottingham. Her main research area is the use of multi-agent systems to solving real-world production scheduling problems. She has carried out successful research in the field of multi-agent systems and their applications to solving production scheduling problems since 1995. She has previously supervised many students in the areas of artificial intelligence, mobile robots, and multi-agent systems. She has published a number of papers in International distinguished scientific journals and refereed conference proceedings. She is currently working on the use of multi-agent systems for Grid scheduling.

Dr Sanja Petrovic is a Senior Lecturer in Computer Science and is a member of the Automated Scheduling, Optimisation and Planning Research Group at the University of Nottingham. Her main area of research includes case-based reasoning, multicriteria decision analysis and modelling of uncertainties by fuzzy sets and logic in general, and their application to scheduling problems in particular. Dr Petrovic is principal investigator and co-investigator on a number of externally funded grants awarded by EPSRC, BBSRC and DTI. She was a guest co-editor for a feature issue of the European Journal of Operational Research (EJOR) on ‘Timetabling and Rostering’ and will be a guest editor for a special issue of the Journal of Scheduling on ‘Expert Systems and Machine Learning in Scheduling’ and a guest co-editor for Annals of Operations Resarch on ‘Personnel Scheduling and Planning’. She has published 15 papers in international scientific journals and over 35 papers in international conference proceedings.