Abstract
This study describes the application of the develop ed model to the scheduling of the Robotic Flexible Assembly Cells (RFACs). The present study is a continuation of the paper titled “Development of a Fuzz ySimulation Model of Scheduling Robotic Flexible Assembly Cells”. This new model for multi-objective scheduling problems in RFACs was based on combining a fuzzy-based mathematical model with simulation software tools. In this study, a hypothetical case study of RFACs is presented to demonstrate the effectiveness of the developed model and then compare the results that are obtained by the new methodology with the common scheduling rules. The simulation results show that the performance of the proposed methodology outperforms the most popular scheduling rules from previous research.
Highlights
Due to increasing competition in the developing global economy, today’s companies are facing greater challenge than ever to employ Flexible Manufacturing Systems (FMS) capable of dealing with unexpected events and meeting customers’ requirements
The discussion will focus on analysing the results and comparing the Robotic Flexible Assembly Cells (RFACs) performance based on the proposed rule (FSR) and existing scheduling rules
From this figure it can be seen that the developed Rule (FSR) obtains the best results for minimising the makespan, compared with the other scheduling rules
Summary
Due to increasing competition in the developing global economy, today’s companies are facing greater challenge than ever to employ Flexible Manufacturing Systems (FMS) capable of dealing with unexpected events and meeting customers’ requirements. FMSs have attracted significant attention by both researchers and industrial practitioners in recent years One class of such systems is called Robotic Flexible Assembly Cells (RFACs). As mentioned in the previous work presented in paper titled “Development of a New Model of Scheduling Robotic Flexible Assembly Cells”, The objective was to propose a new intelligent model of scheduling RFACs in a multi-product assembly environment, using fuzzy logic and simulation software. Khalid Abd et al / Journal of Computer Science 9 (12): 1769-1777, 2013 a fuzzy-based mathematical model with simulation software.
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