281256 Data Management in the Processes for Enterprise Wide Optimization Using an Ontological Framework

Wednesday, October 31, 2012: 3:40 PM
328 (Convention Center )
Edrisi Muñoz, Information Technologies, Centro de Investigacion en Matematicas A.C., Guanaguato, Mexico; Software Engineering, Centro de Investigación en Matemáticas A.C., Guanajuato, Mexico, Elisabet Capon, Chemical Engineering, Universitat Politecnica de Catalunya, Barcelona, Spain, José Miguel Laínez, School of Chemical Engineering, Purdue University, West Lafayette, IN, Antonio Espuña, Chemical Engineering Department, Univesitat Politècnica de Catalunya, Barcelona, Spain and Luis Puigjaner, Chemical Engineering Department, Universitat Politècnica de Catalunya - ETSEIB, Barcelona, Spain

Nowadays the process industries are very complex systems that involve complex decision-making. They require some advanced methods and/or tools in order to integrate and optimize information from different decision levels. In this sense, semantic-based decision support systems seem to offer an efficient tool for facilitating interoperability across multiple, heterogeneous systems. Moreover, enterprises consist of multiple businesses and process units working together at different time and space scales. The organization of the different scales and levels within such complex systems is crucial to understand, analyse, synchronize and improve their operations.

In order to deal with the process complexity, it is necessary to decouple the system across a hierarchy of appropriately chosen levels without disregarding the interrelationship that exists among them. For this purpose, we consider as basis of our analysis the supply chain (SC) concept which can be defined as the group of interlinked resources and activities required to create and deliver products and services to customers. Regarding the integration of different decision levels, an ontology (semantic structure) allows to coordinate the information exchange among the different modeling paradigms/conventions currently used for the different decisions to be taken. Its key role consists of capturing the relevant distinctions of the enterprise structure (specific domain) at the highest level of abstraction, embodying the results of academic research, and offering an operational method to put theory into practice [1].

This work focuses on the integration within the operational decision levels (control, scheduling and supply chain). In this work, an ontological framework is built as the mechanism for information and knowledge models sharing for multiple hierarchical levels and information systems. The potential of the general proposed semantic framework (model maintenance, usability and re-usability) is demonstrated in a case study dealing with an enterprise supply chain network design-planning problem. Further work is underway to unveil the full potential to implement a large-scale semantic web approach to support business processes decisions allowing the mining of quality data which will be translated into useful information by the decision maker. It is important to remark that the generality should be maintained since it is a basic principle in any ontological framework.

[1]      Gruber, T. Ontology, Encyclopedia of Database Systems, Ling Liu and M. Tamerözsu, 2008. Springer-Verlag.

[2]      Munoz, E., Espuna, A., Puigjaner, L. Towards an ontological infrastructure for chemical batch process management. "Computers & chemical engineering", 2010, 34 (5), p. 668-682.

[3]      Brandl, D., Emerson, D., September 2003. Batch markup language batchml.

[4]     International Society for Measurement and Control. (1995). Batch control. Part 1. Models and terminology. International Society for Measurement and Control.

[5]     International Society for Measurement and Control. (2001). Data structures and guidelines for languages. International Society for Measurement and Control.

[6]     International Society for Measurement and Control. (2003). Batch control. Part 3. General and site recipe models and representation. International Society for Measurement and Control.

[7]     International Society for Measurement and Control. (2006). batch Control part 4. Lot production registers. International Society for Measurement and Control.

[8]     International Society for Measurement and Control. (2007). Batch control part 5 automated equipment control models & terminology. International Society for Measurement and Control.

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