Dissertation
Dissertation > Economic > Economic planning and management > Enterprise economy > Production management

Based on the theory of constraints simulation and optimization of production operations

Author LiWenPeng
Tutor GuanZaiLin
School Huazhong University of Science and Technology
Course Industrial Engineering
Keywords Theory of Constraints Production Planning Drum - Buffer - Rope Model Simulation and optimization Response surface methodology
CLC F273
Type Master's thesis
Year 2011
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In many varieties of small batch production environment , the use of Theory of Constraints thinking and DBR control policy has gradually been proven to be an effective production mode of operation. The traditional production planning method can not be quantified DBR key parameters , and simulation and optimization techniques to solve complex manufacturing systems has become the control and management of the effective ways . This paper presents the use of computer simulation and optimization methods and experimental design constraint management model implemented optimization of key parameters to achieve the rationalization of production and operation of the system control parameter configuration provides a scientific research methods and effective means of implementation. Firstly, the theory of constraints DBR control mechanism for the key parameters were studied , and the experimental design , especially related methods on which the response surface method were analyzed. On this basis , proposes a constraint-oriented simulation optimization of manufacturing operations management system framework . Using X-planner and Anylogic establish planning and execution system model two different stages in the production planning model based on the theory of constraints in the use of the operational controls for plan development and generation ; simulation system is responsible for the implementation of the plan implementation process simulation and verification evaluation of different aspects of the production system through simulation allows the simulation process closer to reality . Finally, we use Minitab's response surface module experimental design and response factors obtained between the mathematical model , and thus on the basis of relevant analysis and optimization . Taking a shipyard pipe plus workshop as experimental subjects implementing constraint management thinking , through the proposed optimization system simulation experiments , on which parameters associated with the constraint management quantitatively optimized to transit volume, time buffer into a range for the experimental group factor to on-time delivery rate is needed to optimize the response , obtained reasonable parameter configuration . Finally, in the concluding remarks , based on the simulation and optimization model for this new outlook for future reference for further research and application suggestions .

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