الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 32 |  |  |
| | | from | 32 | | |
Abstract
The time is very important to actual flexible manufacturing system, so proposed approach combining data mining (DM) technique, and the intelligent multi-agent (IMA) simulation platform to find the optimization of production scheduling. After experiments, the results of production scheduling problem can be significantly improved using the proposed method.
A comparison using two architectures Intelligent Multi-Agent (IMA) vs. Fuzzy is given to minimize the total processing time by using SPT (Short Processing Time)for performance measure, and improve the performance of the production lines.
This thesisshowedhow data mining on production data can be used tocapture both explicit and implicit knowledge that is used to create production schedules. Integrated with optimization, new optimized instances selection methodology for scheduling is proposed to identify the best scheduling practice.
This thesis aims topropose a data miningalgorithm (DM) called Business Intelligence of Data Mining (DM). Business Intelligence is a concept of applying a set of technologies to convert data into meaningful information. After defining the goal and identifying the data to be mined, the study applied data mining.
Intelligent algorithm solutions Multi-Agent (IMA) systems lead often to deal with agents having proactive and interactive features that are very useful to know the management systems and the promotion of e-commerce process through data mining. The work in this thesis aims to fill the gap between business intelligence in e-business and data mining method.