The complexity of integrated planning and scheduling models can be tackled with decomposition techniques based on duality and information flows between a master and a set of subproblems. Hence, the information sharing and communication of information from the industrial environments requires flexible structures, facilitating the use of analytic tools and providing higher flexibility for model building in industrial environments. In this work, an ontological framework is proposed to allow the virtualization of systems and processes and to implement a novel Lagrangean decomposition scheme based on hierarchical level decomposition. Indeed, the scheduling and 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 supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.

Peer Reviewed