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|Title:||A hierarchical model for extended supply chain coordination and optimization||Authors:||Yin, Xiao Feng.||Keywords:||DRNTU::Engineering::Industrial engineering::Supply chain
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
|Issue Date:||2011||Source:||Yin, X. F. (2011). A hierarchical model for extended supply chain coordination and optimization. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Supply chain management is about coordinating and managing the entire value chain, from customer order to production, storage, distribution and delivery. However, different function units along a supply chain have their own purpose and operate independently. This research presents an in-depth study aiming at realizing a hierarchical model and a framework for supply chain coordination and optimization. It is envisaged that the proposed model can be used as a tool to facilitate planning, optimize the detailed schedules of the various supply chain units such as manufacturing plants, suppliers and distribution centres and support global manufacturing. Accordingly, a prototype distributed intelligent system for multi-level supply chain coordination, optimization and order scheduling (SCASO) has been established. The prototype SCASO system comprises three main modules, namely Routing and Sequence Optimizer (RSO), Supply Chain Virtual Clustering (SCVC) and Supply Chain Order Scheduler (SCOS). Basically, the RSO module is used to provide the SCVC with a reasonably good routing and order processing sequence combination while taking into account the capacity of each supply chain unit and the business strategy to maintain the required customer service level and competitiveness. The SCVC module then attempts to compartmentalize an extended supply chain optimization problem that can hardly be solved by conventional algorithms into manageable sub-problems. Subsequently, the SCOS module, which contains an agent-based distributed coordination and scheduling mechanism, integrates scheduling with supply chain optimization.||URI:||http://hdl.handle.net/10356/43288||metadata.item.grantfulltext:||open||metadata.item.fulltext:||With Fulltext|
|Appears in Collections:||MAE Theses|
checked on Dec 19, 2019
checked on Dec 19, 2019
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