Predicting the unexpected
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. (C) 2011 Elsevier By. All rights reserved.
prediction modeling design for the unexpected logistic execution systems holonic systems