Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment |
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Affiliation: | 1. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, P.R. China;2. School of Electrical Engineering and Information Engineering, LanZhou University of Technology, Lanzhou, Gansu 730000, P.R. China;3. School of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong 250101, P.R. China;4. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, P.R. China |
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Abstract: | 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. |
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Keywords: | Supply chain planning Process scheduling Ontology Decision-levels integration Lagrangian decomposition |
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