Developing a Two-Stage D-Optimal Design to Choose the Flexible Manufacturing Systems Component

Document Type : Research Paper


1 Ph.D. Candidate in Industrial Management, Faculty of Management, Kish International Campus, University of Tehran, Kish, Iran

2 School of Industrial Engineering, Collage of Engirting, University of Tehran, Tehran, Iran

3 Department of Management Faculty of Management and Finance, Khatam University, Tehran, Iran



Objective: Flexible Manufacturing Systems (FMS) are integrated production workstations with computer-controlled systems that use the equipment components. The central computer controls this system and have significant impacts on improving the production process and its efficiency. Accordingly, a case study in an FMS electrical manufacturing system that have high complexity of production processes is investigated and the degree of automation of the production line is very important. Choosing an optimized number of advanced equipment is a very hard and important issue for their managers to improve the productivity of their production line and have the highest responsibility for customers’ orders and it’s a Hard-to-Change model and changing the equipment costs a lot.
Methods: In this paper the two-stage D-Optimal method is used to optimize the combination of FMS elements and equipment. The input of the D-Optimal response level is calculated in discrete-event simulation results. In fact, for different conditions, the various FMS equipment is allocated to each process. So, each one of the simulation results for elements combination is an input for experimental design. Also, the response level (y) of experiments from the various FMS indexes are calculated by weighting method. All of the combinations are categorized based on the number of automated and manual equipment to decrease the number of experiments and increase the accuracy of data in a case study with Hard to Change parameters. In this paper, two-stage of D-Optimal design is defined. In the fisrt stage, all of the combinations in these categories are investigated and in the second one, the optimized one of the first one is elaborated to determine the best combination. So, all of the experiments in the best category of first stage are simulated and considered in the D-Optimal second stage.
Results: In the first phase, all the FMS, advanced production equipment and elements are considered and after choosing the best calculated “y”, in this category which was 147133.09, another D-Optimal is optimized in the second phase to choose the best combination that its “y” is gained 151317.88 from the best category in the first phase of D-Optimal design. So, it needs to elaborate to the optimize combination achieved from the first phase of D-Optimal. As the results of the developed D-Optimal and second phase of this method show the best combination of equipment is using the automated and FMS equipment approximately 92.8% of total components. Finally, the list of FMS equipment is suggested and its productivity is compared with the productivity of current and another higher degree of automation status for this production line.
Conclusion: In summary, as the results of experiment design shows, using advanced production systems aren’t necessary improve the efficiency of the system and calculating the optimal combinations needs accurate calculations.


Main Subjects