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Proposing a Simulation-based Optimization Model for Determining Optimal Parameters in a Demand-Driven Material Requirements Planning Approach | ||
| Industrial Management Journal | ||
| دوره 15، شماره 4، 2023، صفحه 621-649 اصل مقاله (1.53 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/imj.2024.361277.1008063 | ||
| نویسندگان | ||
| Maryam Younespour1؛ Majid Esmaelian* 2؛ Kamran Kianfar3 | ||
| 1Ph.D. Candidate, Department of Industrial Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran. | ||
| 2Associate Prof., Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran. | ||
| 3Assistant Prof., Department of Industrial Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran. | ||
| چکیده | ||
| Objective The demand-driven material requirements planning approach (DDMRP) considers inventory buffers at certain points in the bill of materials to respond more quickly to customer demands and demand fluctuations. In this approach, the presence of a buffer at each point of the bill of material does not necessarily improve the material flow and may lead to an increase in inventory costs. Moreover, this approach includes parameters that are experimentally set by the manager, and inappropriate values for these parameters can result in deficient performance. Therefore, this paper aims to simultaneously determine optimal values for the parameters of this approach at both the strategic level (strategic inventory positioning) and the operational level (planning phase) to minimize inventory cost and achieve a 100% service level within customer tolerance time. Methods In this research, a simulation-optimization model is introduced to determine the optimal values of three fundamental parameters: strategic inventory position, variability, and lead time factors. To address this, a combination of genetic algorithm and mixed-integer linear programming with a CPLEX solver is utilized. In the genetic algorithm phase, the allowed positions of the buffer are randomly selected in a way that the delivery of the final product is less than the customer tolerance time. In the mixed-integer linear programming phase, the optimal values of variability and lead time factors are determined with the aim of minimizing inventory cost and avoiding stockout issues. Results The proposed model is evaluated across 12 randomly generated instances of the bill of materials, each varying in levels and the number of parts. This set includes the specific case studied in the article by Jiang and Rim. The performance of our presented model is then compared with that of Jiang and Rim's model. The results consistently reveal that the proposed model demonstrates superior efficiency across all instances. The comparison of results with the data from the main article proves that the inventory cost of the proposed model has decreased between 82% and 86%, with an average reduction of 83.6%. The improvement percentage of on-time deliveries ranges from 0% to 4%, with an average improvement of 2.2%. Comparison with randomly generated data indicates that the average inventory cost using the proposed model is reduced between 73% and 91%, with an average reduction of 81.8%. Moreover, with 100% confidence, all orders are fulfilled within a time frame shorter than the customer tolerance time. Conclusion In the presented model, despite more restrictions regarding the buffer position in the bill of material, the performance results of the proposed model demonstrate a significant reduction in average inventory costs compared to the Jiang and Rim model. Additionally, with full confidence, all orders will be fulfilled. The methodology employed in this research can function as a decision-support tool for managers. It aids in determining the optimal quantity and timing of manufacturing or purchasing orders, minimizing inventory costs while aiming to achieve a 100% service level within the customer tolerance time | ||
| کلیدواژه‌ها | ||
| Decoupled buffer point؛ Decoupled lead time؛ Demand-driven Material Requirement Planning (DDMRP)؛ Production planning | ||
| مراجع | ||
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ایل بیگی نژاد، عباس؛ ایزدبخش، حمیدرضا و ارشدی خمسه، علیرضا (1400). طراحی و توسعه مدل توزیع منابع مالی به تأمین‌کنندگان در یک زنجیره تک‌تولیدکننده. مدیریت صنعتی، 13 (4)، 537-557.
بنی هاشمی، سید علی و حاجی مولانا، سیدمحمد (1396). تحلیل حساسیت اثر شلاق چرمی در زنجیره تأمین چهارسطحی با استفاده از روش میانگین متحرک برای برآورد تقاضا. مدیریت صنعتی، 9 (1)، 43-58.
بهادران، مریم؛ فدایی اشکیکی، مهدی؛ طالقانی، محمد و همایون فر، مهدی (1401). طراحی شبکه زنجیره تأمین حلقه بسته تاب‌آور تحت شرایط ریسک‌های عملیاتی و اختلال با رویکرد مالوی. مدیریت صنعتی، 14(4)، 595-617.
بهاری، آرمان و علیدوست، احسان (1399). شناسایی الگوی کاهش تلفات زمان تولید با ترسیم نقشه وضعیت آینده شرکت سازه‌های فلزی طاق بیست بیرجند. فصلنامه مطالعات مدیریت راهبردی، 11(41)، 93-110.
رشیدی کمیجانی، علیرضا و قربانی، محمد (1391). اولویت‌بندی سیستم‌های کنترل تولید و موجودی کششی و هیبریدی در شرکت ایران خودرو. پژوهشگر (مدیریت)، 9(25)، 35-44.
گلچوب فیروزجائی، بختیار؛ شعار، مریم و رجب‌زاده قطری، علی (1401). مدلسازی اثر موجی اختلال تأمین‌کننده بر توزیع‌کننده در زنجیره تأمین سه‌سطحی. مدیریت صنعتی، 14 (4)، 638-668.
محمدی، طاهره؛ سجادی، سید مجتبی؛ نجفی، سیداسماعیل و تقی‌زاده یزدی، محمدرضا (1401). بهینه‌سازی زنجیره تأمین هوشمند تحت سیاست‌مدیریت موجودی توسط فروشنده با رویکرد انتخاب فناوری مرتبط با اینترنت اشیا. مدیریت صنعتی، 14(3)، 458-483.
یوسفی زاده، سحر و ملانظری، مهناز (1397). روند تکامل سیستم برنامهریزی منابع سازمانی و مزایا و معایب این سیستم بعد از مراحل پیاده‌سازی. پژوهش حسابداری، 8(1)، 1-25.
References Achergui, A., Allaoui, H. & Hsu, T. (2021). Optimisation of the Automated Buffer Positioning Model under DDMRP Logic. IFAC-PapersOnLine, 54(1), 582-588. Azzamouri, A., Baptiste, P., Dessevre, G. & Pellerin, R. (2021). Demand driven material requirements planning (Ddmrp): A systematic review and classification. Journal of Industrial Engineering and Management, 14(3), 439–456. Bahadoran, M., Fadaei Ashkiki, M., Taleghani, M. & Homayounfar, M. (2023). Designing a Resilient Closed-Loop Supply Chain Network under Operational Risk and Disruption Conditions by the Mulvey Approach. Industrial Management Journal, 14(4), 595-617. Bahari, A. & Alidoost, E. (2020). Identify the pattern of reducing production time losses by mapping the future situation in TaghBist Steel Structures Company. Strategic management studies, 11 (41), 93-110. (in Persian) Bahu, B., Bironneau, L. & Hovelaque, V. (2019). Compréhension Du DDMRP Et De Son Adoption:Premiers éléments Empiriques. Logistique & Management, 27 (1), 20-32. Banihashemi, S.A. & Haji Molana, S.M. (2020). Analyzing Bullwhip Effect Sensitivity in a Four-level Supply Chain Using Average Moving Method to Forecast the Demand. Industrial Management Journal, 8(1), 1-25. (in Persian) Benavente, D., Peralta, S., Quispe, G., Moguerza, J., & Raymundo, C. (2023). The Demand Driven MRP Implementation in Complex Manufacturing Industries: A Systematic Literature Reviews. International Journal of Engineering Trends and Technology, 71, 33–45, DOI: 10.14445/22315381/IJETT-V71I3P205. Castro, P.M. (2015). Tightening piecewise mccormick relaxations for bilinear problems. Computers & Chemical Engineering, 72, 300-311. Damand, D., Lahrichi, Y., & Barth, M. (2023). Parameterisation of demand-driven material requirements planning: a multi-objective genetic algorithm. International Journal of Production Research, 61(15), 5134-5155. Dang, Y., Batson, R. G., Chen, D. (2011). Applied Integer Programming: Modeling and Solution. United States: Wiley. Dehnad, K. (2012). Quality Control, Robust Design, and the Taguchi Method. Springer Science & Business Media. Duhem, L., Benali, M. & Martin, G. (2023). Parametrization of a demand-driven operating model using reinforcement learning. Computers in Industry, 147, 103874. EilbeygiNejad, A., Izadbakhsh, H. & Arshadi Khamseh, A. (2022). Designing and Developing a Model for Distributing Financial Resources among Suppliers in a Single Producer Chain. Industrial Management Journal, 13(4), 537-558. (in Persian) Golchoub Firozjaei, B., Shoar, M. & Rajabzadeh Ghatari, A. (2022). Ripple Effect Modeling of Supplier Disruption on the Distributor in the Three-stage Supply Chain. Industrial Management Journal, 14(4), 638-668. (in Persian) Iguaran Munoz, J. A. (2023). Development of a calculation model for material management using Demand Driven MRP (DDMRP) for a company in the Food Industry. Politecnico di Torino. Jiang, J. & Rim, S. C. (2016). Strategic inventory positioning in BOM with multiple parents using ASR lead time. Mathematical Problems in Engineering, 2016, 1024-123X, DOI: 10.1155/2016/9328371. Kortabarria, A., Apaolaza, U., Lizarralde, A., & Amorrortu, I. (2018). Material management without forecasting: From MRP to demand driven MRP. Journal of Industrial Engineering and Management, 11(4), 632–650. Lahrichi, Y., Damand, D. & Barth, M. (2022). A first MILP model for the parameterization of Demand-Driven MRP. Computers and Industrial Engineering, 174, 108769. Lee, C. J., & Rim, S. C. (2019). A mathematical safety stock model for DDMRP inventory replenishment. Mathematical Problems in Engineering, 2019, 1024-123X, DOI: 10.1155/2019/6496309. Liu, Y., Dai, J., Zhao, S., Zhang, J., Shang, W., Li, T., ... & Wang, Z. (2020). Optimization of five-parameter BRDF model based on hybrid GA-PSO algorithm. Optik, 219, 164978. Marzougui, M., Messaoudi, N., Dachry, W., Sarir, H. & Bensassi, B. (2020). Demand Driven Mrp: Literature Review And Research Issues. 13ème Conference Internationale De Modelisation, Optimisation Et Simulation (MOSIM2020), 12-14 Nov 2020, AGADIR, Maroc, Nov 2020, AGADIR (virtual), Morocco. Meinzel, L. (2019). Treball de Fi de Màster DDMRP: presentation of a new solution of stock management and master production scheduling MEMÒRIA Autor. Universitat Politècnica de Catalunya. Miclo, R., Fontanili, F., Lauras, M., Lamothe, J. & Milian, B. (2016). An empirical comparison of MRPII and Demand-Driven MRP. IFAC-PapersOnLine, 49(12), 1725–1730. Miclo, R., Fontanili, F., Lauras, M., Lamothe, J., & Milian, B. (2016). MRP vs. Demand-driven MRP: Towards an objective comparison. Proceedings of 2015 International Conference on Industrial Engineering and Systems Management, IEEE IESM 2015, 1072–1080. Miclo, R., Lauras, M., Fontanili, F., Lamothe, J., & Melnyk, S. A. (2019). Demand Driven MRP: assessment of a new approach to materials management. International Journal of Production Research, 57(1), 166–181. Mohammadi, T., Sajadi, S.M., Najafi, S.E. & Taghizadeh Yazdi, M. (2022). Optimizing Smart Supply Chain with Vendor Managed Inventory through the Internet of Things. Industrial Management Journal, 14(3), 458-483. (in Persian) Nagarajan, H., Lu, M., Yamangil, E. & Bent, R. (2016). Tightening McCormick relaxations for nonlinear programs via dynamic multivariate partitioning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9892 LNCS, 369–387. Ohno, T., & Bodek, N. (2019). Toyota Production System: Beyond Large-Scale Production. Productivity press. https://doi.org/10.4324/9780429273018. Ptak, C. A., & Smith, C. (2019). Demand driven material requirements planning (DDMRP). Industrial Press. Rashidi Kamijani, A. R., Ghorbani, M. (2005). Prioritization of production control systems and hybrid and traction inventory in Iran Khodro Company. Journal of Management, 25, 35-44. (in Persian) Shofa, M. J., Moeis, A. O., & Restiana, N. (2018). Effective production planning for purchased part under long lead time and uncertain demand: MRP Vs demand-driven MRP. IOP Conference Series: Materials Science and Engineering, 337(1), 012055. Shofa, M.J. & Widyarto, W.O. (2017). Effective Production Control in an Automotive Industry: MRP vs. Demand-Driven MRP. In AIP Conference Proceedings, Vol. 1855. AIP Publishing LLC. Simchi Levi, D. & Kaminsky, P. (2000). Designing and managing the supply chain, New York, Mc Graw Hill. Sivanandam, S. N., & Deepa, S. N. (2008). Genetic Algorithms. Introduction to Genetic Algorithms, 15–37. https://doi.org/10.1007/978-3-540-73190-0_2. Stevenson, M., Hendry, L. C., & Kingsman, B. G. (2005). A review of production planning and control: The applicability of key concepts to the make-to-order industry. International Journal of Production Research, 43(5), 869–898. Thürer, M., Fernandes, N. O., & Stevenson, M. (2020). Production planning and control in multi-stage assembly systems: an assessment of Kanban, MRP, OPT (DBR) and DDMRP by simulation. International Journal of Production Research, 0020-7543. Velasco Acosta, A. P., Mascle, C., & Baptiste, P. (2020). Applicability of Demand-Driven MRP in a complex manufacturing environment. International Journal of Production Research, 58(14), 4233–4245. Xua, G., Guana, Z., Yueb, L. & Mumtazc, J. (2023). An efficient production planning approach based demand driven MRP under resource constraints. International Journal of Industrial Engineering Computations, 14(3), 451–466. Yousefizadeh, S. & Molanazari, M. (2018). The Evolution of Enterprise Resource Planning System and the Advantages and Disadvantages of the System after Implementation Steps. Journal of Accounting and Social benefit, 18(1), 1-25. (in Persian) | ||
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