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The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: In the domain of chemical process engineering, there is an increased interest in the integration of the enterprise hierarchical levels for decision-making purposes. At the scheduling level, decisions on the allocation of tasks to resources, sequencing and timing of tasks must be managed. However, such decisions are directly related to other enterprise actions, such as control and planning, but they are difficult to coordinate because they are modeled at different time and space scales, and their goals are not the same. In order to achieve integrated decisions supported by high quality information, there is a need to improve and develop robust computational tools and consistent models. In general, scheduling optimization approaches for decision-making differ depending on problem features, such as physical layout or time representation. Therefore, this work focuses on providing a framework based on a semantic model that captures the diversity in scheduling problem representation. Such semantic model uses the master recipe concept from the ANSI/ISA-88 standard perspective and encapsulates the scheduling decision task features. As a result, by the use of a single representation approach, any scheduling problem can be modeled and solved by its adequate optimization tool. The potential of a general model representation is presented by means of several case studies related to the scheduling function. Such case studies shed light to the model capabilities to represent different kinds and particular scheduling problems, achieving integration at the different decision support levels. [Copyright &y& Elsevier]

Abstract: The complexity of decision-making in process industries and the need of highly competitive organizations require new supporting tools to coordinate and optimize the information flow among decision levels. This work presents a framework for integrating the scheduling and control decision levels by means of an ontology, which allows and coordinates the information exchange among the different modeling paradigms/conventions currently used for the enterprise-wide optimization (EWO). The scheduling of two multiproduct batch plants with increasing complexity is presented for illustrating the proposed working procedure. [Copyright &y& Elsevier]