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Designing a Two-Stage D-optimal Approach for Selecting Components of Flexible Manufacturing Systems | ||
| Industrial Management Journal | ||
| دوره 16، شماره 4، 2024، صفحه 535-559 اصل مقاله (1.11 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/imj.2023.349193.1007992 | ||
| نویسندگان | ||
| Nima Pasha1؛ Fariborz Jolai2؛ Seyyed Hosein Razavi Haji Agha* 3 | ||
| 1Ph.D. Candidate, Department of Industrial Management, Faculty of Management, Kish International Campus, University of Tehran, Kish, Iran. | ||
| 2Prof., Department of Industrial Engineering, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran. | ||
| 3Associate Prof., Department of Management, Faculty of Management and Financial Sciences, Khatam University, Tehran, Iran. | ||
| چکیده | ||
| Objective Flexible Manufacturing Systems (FMS) are integrated workstations that utilize computer-controlled equipment components for production. These systems are managed by a central computer, which significantly enhances the efficiency and productivity of the production process. Accordingly, a case study is conducted on an FMS electrical manufacturing system with complex manufacturing processes, where automation on the production line is crucial. Selecting the optimal number of advanced equipment is a challenging and vital issue for managers aiming to boost productivity and efficiently fulfill customer orders. It is a hard-to-change model, and replacing equipment incurs substantial costs. Methods This study employs the two-stage D-Optimal method to optimize the combination of FMS elements and equipment. The D-Optimal response level input is derived from discrete-event simulation results. Depending on the conditions, various FMS equipment is allocated to each process. Each simulation result for element combinations serves as input for the experimental design. Additionally, the response level (y) of experiments from various FMS indexes is calculated using a weighting method. To reduce the number of experiments and increase data accuracy in a case study with hard-to-change parameters, all combinations are categorized based on the number of automated and manual equipment. The two stages of the D-Optimal design are defined as follows: In the first stage, all combinations within these categories are investigated. In the second stage, the optimized combination from the first stage is analyzed to determine the best configuration. Experiments in the top category from the first stage are simulated and further evaluated in the second stage of the D-Optimal method. Results In the first phase, all advanced production equipment and FMS elements were considered. After selecting the best-calculated “y” value, which was 147,133.09 in this category, another D-Optimal design was optimized in the second phase to determine the best combination. This combination yielded a “y” value of 151,317.88, representing an improvement over the best category in the first phase of the D-Optimal design. Consequently, the optimized combination from the first phase was further refined. The results from the developed D-Optimal method and the second phase indicate that the optimal combination of equipment involves using automated and FMS equipment for approximately 92.8% of the total components. Finally, a list of recommended FMS equipment is provided, and its productivity is compared with the productivity at the current level and a higher degree of automation for this production line. Conclusion In summary, the results of the experimental design show that using advanced production systems does not necessarily improve system efficiency, and determining optimal combinations requires accurate calculations. | ||
| کلیدواژه‌ها | ||
| Computer-aided design of experiments؛ Flexible manufacturing systems؛ Two-stage D-optimal design؛ Work-in-Process | ||
| مراجع | ||
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