(397e) Operations Research Ontology for Improving Optimization Frameworks
Enterprise-wide optimization (EWO) is encompassed by different areas which allow the mail goal as a global optimization. Thus EWO involves optimizing the operations of supply, manufacturing and distribution in a company (Grossmann, 2005). Specifically EWO lies at the interface of chemical engineering and operations research, as a problem solving approach. A challenge in EWO consists in developing flexible modeling environments for the problem representation which is ultimately the basis for reaching efficient decision-making. A key feature in EWO is the integration of information and decision-making along the various functions and hierarchical levels of firms’ supply chains. However, further development is still necessary to easily develop, build and integrate different enterprise models. Such integration should reflect the complex tradeoffs and interactions across the components of the enterprise.
This work proposes the use of an Operations Research Ontology (ORO) as a step forward in capturing the nature of problems and technologies for decision making in the enterprise represented as qualitative issues for decision theory. This ORO model interacts with other two semantic models previously developed, specifically the Enterprise Ontology Project (Muñoz et al. 2013) and the Ontological Math Representation (Muñoz et al. 2012), thus enhancing the functionalities of the ontological framework. The integration among the three semantic models structure the necessary information for a problem representation and its future solution. Specifically, these issues comprise the real system, a mathematical representation, and finally the problem design representation.
A brief description of the task of each model are explain next. The first model, enterprise ontology project (EOP) aims at reaching a formal conceptualization of the real system of the process industry under study. This step encompasses the standardized semantic description of the system using the, and the definition and acquisition of the required dynamic and static data. The second model ontological mathematical representation (OMR) pursues the formalization of the mathematical equations describing the system abstraction. The OMR can capture both mathematical expressions of mathematical descriptions already in use and new mathematical descriptions of the system conceptualization. The third model operational research ontology (ORO) aims to structure the optimization model system along with the mathematical semantic model as the basis for instantiating and obtain a semantic decision model for optimization purposes. Finally, the mathematical programming standards are applied to the semantic decision model and the problem is solved to reach the optimal integrated solution which assists managers in making the decisions to be deployed in the real system. As a result the proposed ontological framework provides a tool to build computational optimization models for the enterprise decision-making and to allow the comprehensive application of enterprise wide optimization throughout the process industry. Moreover, this extended framework also allows the building of more accurate models for the chemical process industry and the full integration and solution of large-scale optimization models
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