Push and pull production systems: issues and comparisons
Operations Research
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Reference architecture for holonic manufacturing systems: PROSA
Computers in Industry - Special issue on manufacturing systems
ESAW '00 Proceedings of the First International Workshop on Engineering Societies in the Agent World: Revised Papers
Modeling uncertain domains with polyagents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Towards robust and efficient planning execution
Engineering Applications of Artificial Intelligence
Intelligent products: Agere versus Essere
Computers in Industry
Operating System Concepts
A Holonic Chain Conveyor Control System: An Application
HoloMAS '09 Proceedings of the 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing
SASO '09 Proceedings of the 2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems
| Hi-index | 0.00 |
Dagstuhl seminar no. 10102 on discrete event logistic systems recognized a network of persistent models to be a ''Grand Challenge.'' Such on-line model network will offer an infrastructure that facilitates the management of logistic operations. This ambition to create a network of persistent models implies a radical shift for model design activities as the objective is an infrastructure rather than application-specific solutions. In particular, model developers can no longer assume that they know what their model will be used for. It is no longer possible to design for the expected. This paper presents insights in model development and design in the absence of precise knowledge concerning a model's usage. Basically, model developers may solely rely on the presence of the real-world counterpart mirrored by their model and a general idea about the nature of the application (e.g. coordination of logistic operations). When the invariants of their real-world counterpart suffice for models to be valid, these models become reusable and integrate-able. As these models remain valid under a wide range of situations, they become multi-purpose and durable resources rather than single-purpose short-lived components or legacy, which is even worse. Moreover and more specifically, the paper describes how to build models that allow their users to generate predictions in unexpected situations and atypical conditions. Referring to previous work, the paper concisely discusses how these predictions can be generated starting from the models. This prediction-generating technology is currently being transferred into an industrial MES.