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The Optimization Study Of Dynamic Business Processes Based On Petri Nets And Bayesian Inference

Posted on:2012-12-14Degree:MasterType:Thesis
Country:ChinaCandidate:L TangFull Text:PDF
GTID:2219330338966583Subject:Logistics Engineering
Abstract/Summary:PDF Full Text Request
Many uncertain factors affect the development of enterprises according to the economic globalization process and the acceleration of demand in market, which makes enterprises to change their management strategies as soon as possible to respond to the great market competition. Dynamic business process management is an effective method in the process; the basic idea is quantitative analysis of a variety of uncertain information from market or other; then adjusts and optimizes timely to realize the dynamic management of business processes according to the information to adapt to the development of market and ultimately to enhance customer service level and to increase market share and profits.The paper defines the dynamic business process management, analysis the factors and process type of dynamic business processes through reading and analyzing a large number of domestic and foreign literatures and combining with the knowledge learned; and combines with Petri net modeling graphical tools to establish dynamic business process network model to analyze the characteristics of it. For the dynamic business process optimization, the paper achieves the combination of Bayesian inference and Petri nets. First, it establishes the corresponding Bayesian network inference model and gives reasoning algorithm parameters; then feedbacks these parameters to the Petri net model to determine the optional path through the dynamic business process optimization that given; so that enterprises can manage their companies dynamically to maximum economic benefits. Finally, the paper confirms the feasibility of applying Petri net and Bayesian inference to the dynamic optimization of business processes through actual case-PC Demolition. In the case, it establishes used computer disassembly model which based on Petri net, finds out the defects in computer through Bayesian inference, then feedbacks the defects to the Petri net to obtain the optimal process path through computer programming operations. And changes the optimal disassembly path in computer dynamically through changing uncertain information timely and learns the defect parameters inference constantly to the demolition of lowest demolition cost and highest efficiency. Applying Petri net and Bayesian inference to the dynamic optimization of business processes is a new process optimization method, which is not only providing reliable management solutions, but also a new research idea in dynamic business process optimization.
Keywords/Search Tags:dynamic business process, Petri net, Bayesian inference, computer disassembly
PDF Full Text Request
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