Symbiotic simulation for decision support in high-tech manufacturing and service networks

• Main Author: Zeng, Fanchao
• Other Authors: Stephen John Turner
• Format: Final Year Project
• Language: English
• Published: 2009
• Subjects: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling; DRNTU::Engineering::Computer science and engineering::Computer applications::Physical sciences and engineering
• Online Access: http://hdl.handle.net/10356/16844
• Institution: Nanyang Technological University

With the increasing growth of manufacture networks as well as the global competitions in the lubricant industry, the management of supply chain is vital for large vertically-integrated petroleum companies. Operational decision-making should consider the entire supply chain which includes upstream raw material suppliers, downstream customers as well as the internal entities of the specialty chemicals company. A simulation model of the entire supply chain can serve as a valuable quantitative tool to aid offline analysis and optimization. However, solutions are still needed for on-line decision support and automate control. Symbiotic simulation system can be the solution. In this report, we present a symbiotic simulation control system (SSCS) which is based on a generic framework for symbiotic simulation being developed by Parallel and Distributed Computing Centre in Nanyang Technological University. An application-specific modification in scenario management, SCEM has been made to the SSCS. Together with the generic solution provided by the generic framework, an integrated SSCS is developed and integrated with a Jadex-based global lubricant supply chain simulation model. It utilizes proactive what-if analysis to improve the performance of inventory management and reactive what-if analysis to find solutions to low finished product fill rate. The experimental results demonstrate that this control system can achieve notable performance improvement over common practice and can be used to provide decision support and control in near real-time. In the current version of the SSCS, parameters such as threshold condition to trigger what-if analysis, the what-if simulation duration and physical system lock-up period are preset and fixed based on past experience or personal preference. However, the preset parameters may not be the optimal configurations for SSCS. Hence, SSCS should be able to dynamically find the optimal parameters. Moreover, based on current physical state, automate readjustment to the SSCS itself is required for the real-time implementation of SSCS. Since SSCS can extensively simulate and evaluate different scenarios, it can be the solution to its own problem as well. An application-specific two-level SSCS implementation is proposed.