(234b) Information Management for Environmental Impact Assessment in an Integrated Enterprise Structure Based On an Ontological Framework | AIChE

(234b) Information Management for Environmental Impact Assessment in an Integrated Enterprise Structure Based On an Ontological Framework

Authors 

Muñoz, E. - Presenter, Universitat Politecnica de Catalunya
Laínez, J. M., Purdue University
Puigjaner, L., Universitat Politecnica de Catalunya


Ontologies stand for an excellent choice for building complex models maintaining a high level of flexibility, re-usability, usability and easiness of maintenance.

Enterprises are highly involved systems, in which decision-making  becomes a highly challenging task, and decision processes are usually separated in several levels or subprocesses. Such levels share data and information, which requires an efficient system integration and communication structure.

Therefore, effective integration among the different hierarchical levels, by means of tools improving information sharing and communication, may play a crucial role for the enhanced enterprise operation, and consequently for fulfilling the enterprise's goals [1].

This work proposes the re-use of an ontological model for the integrated enterprises structure in order to include the environmental assessment function. The ontological framework provides a common modeling framework which facilitates integration among the different decision levels, and works as the mechanism for information and knowledge sharing for multiple applications in the enterprise [2]. The general semantic framework developed is applied to an enterprise supply chain network design-planning problem case study considering environmental issues.

References:

[1] Grossmann, I. Enterprise-wide optimization: A new frontier in process systems engineering Aiche Journal, 2005, 51, 1846-1857.

[2] Muñoz, E., Capon-Garcia, E., Lainez, J., Espuña, A., Puigjaner, L., 2011a. Ontological framework for the enterprise from a process perspective. In: SciTePress (Ed.), Proceedings of the International Conference on Knowledge Engineering and Ontology Development. pp. 538 – 546.