Division of Information Technology, Engineering and the Environment
School of Advanced Manufacturing and Mechanical Engineering
School of Advanced Manufacturing and Mechanical Engineering
Thesis Abstract
Today’s companies develop flexible systems that are adaptable to assemble a mix of products with minimal reconfiguration. A Robotic Flexible Assembly Cell (RFAC) is an adaptable system which can assemble a variety of products using the same resources. A major limitation of Scheduling RFACs is that no prior research has documented the scheduling problem for assembly of multi-products. Secondly, few academic papers considered flexible assembly cells as a dynamic environment. Finally, no research investigated artificial intelligence approach to solve the job shop scheduling problem. Hence, the objective of the present study is to develop a methodology for intelligent dynamic scheduling of robotic flexible assembly cells for concurrent assembly of multi-products. To achieve the above aim, three main stages will be considered in this study, the first stage is modeling of dynamic job shop scheduling of RFAC. Fuzzy logic technique is considered as a powerful optimisation tools. The second stage is evaluating RFAC performance with different suggested performance measures, in this study; Simulink/Simevents will be used as discrete events simulation software to evaluate the performance of RFAC. While the last stage, the study will be proposed hybrid intelligent techniques using a Neural Fuzzy system to determine the optimum scheduling strategies for RFAC. Finally, a case study will be presented at the last part of the study to demonstrate the implementation of the suggested approach.
