New Hybrid Method for Buffer Positioning and Production Control Using DDMRP Logic in Smart Manufacturing
Despite its proven effectiveness in inventory management across various industries, Demand-Driven Material Requirements Planning (DDMRP) remains largely a manual process, with few studies investigating its numerical integration. This research proposes a novel multi-stage production control framework...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/9/7/219 |
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| author | Sahar Habbadi Ismail El Mouayni Brahim Herrou Souhail Sekkat |
| author_facet | Sahar Habbadi Ismail El Mouayni Brahim Herrou Souhail Sekkat |
| author_sort | Sahar Habbadi |
| collection | DOAJ |
| description | Despite its proven effectiveness in inventory management across various industries, Demand-Driven Material Requirements Planning (DDMRP) remains largely a manual process, with few studies investigating its numerical integration. This research proposes a novel multi-stage production control framework grounded in DDMRP principles, enabling effective scheduling of production orders based on either demand forecasts or actual demand, when available. A mixed-integer programming (MIP) model is developed to capture the dynamic interactions between demand, buffer positioning, and replenishment policies, supporting reactive production planning in smart, reconfigurable manufacturing environments. To identify the optimal buffer locations, a Genetic Algorithm (GA) is employed. The MIP model provides the GA with production planning outputs used to evaluate the fitness of decisions regarding buffer placement. To demonstrate the effectiveness of this hybrid GA–MIP approach, simulations are conducted on three representative production configurations. The results show that the proposed method significantly improves the theoretical performance of each configuration by determining optimal buffer locations and planning replenishments, achieving a better balance between inventory levels and demand fulfillment. |
| format | Article |
| id | doaj-art-e0888b320a7b4af786c60f1f86ee8412 |
| institution | DOAJ |
| issn | 2504-4494 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj-art-e0888b320a7b4af786c60f1f86ee84122025-08-20T03:08:06ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942025-06-019721910.3390/jmmp9070219New Hybrid Method for Buffer Positioning and Production Control Using DDMRP Logic in Smart ManufacturingSahar Habbadi0Ismail El Mouayni1Brahim Herrou2Souhail Sekkat3Industrial Engineering/Faculty of Science and Technology, University Sidi Mohammed Ben Abdellah, Fès B.P. 2202, MoroccoCRAN—Research Centre for Automatic Control of Nancy, 57070 Metz, FranceIndustrial Engineering/Faculty of Science and Technology, University Sidi Mohammed Ben Abdellah, Fès B.P. 2202, MoroccoIndustrial Engineering/Ecole Nationale Supérieure d’Arts et Métiers, University Moulay Ismail, Meknès B.P. 15290, MoroccoDespite its proven effectiveness in inventory management across various industries, Demand-Driven Material Requirements Planning (DDMRP) remains largely a manual process, with few studies investigating its numerical integration. This research proposes a novel multi-stage production control framework grounded in DDMRP principles, enabling effective scheduling of production orders based on either demand forecasts or actual demand, when available. A mixed-integer programming (MIP) model is developed to capture the dynamic interactions between demand, buffer positioning, and replenishment policies, supporting reactive production planning in smart, reconfigurable manufacturing environments. To identify the optimal buffer locations, a Genetic Algorithm (GA) is employed. The MIP model provides the GA with production planning outputs used to evaluate the fitness of decisions regarding buffer placement. To demonstrate the effectiveness of this hybrid GA–MIP approach, simulations are conducted on three representative production configurations. The results show that the proposed method significantly improves the theoretical performance of each configuration by determining optimal buffer locations and planning replenishments, achieving a better balance between inventory levels and demand fulfillment.https://www.mdpi.com/2504-4494/9/7/219Demand-Driven MRP (DDMRP)mixed-integer programming (MIP)Genetic Algorithmbuffer positioningproduction planningreconfigurable manufacturing |
| spellingShingle | Sahar Habbadi Ismail El Mouayni Brahim Herrou Souhail Sekkat New Hybrid Method for Buffer Positioning and Production Control Using DDMRP Logic in Smart Manufacturing Journal of Manufacturing and Materials Processing Demand-Driven MRP (DDMRP) mixed-integer programming (MIP) Genetic Algorithm buffer positioning production planning reconfigurable manufacturing |
| title | New Hybrid Method for Buffer Positioning and Production Control Using DDMRP Logic in Smart Manufacturing |
| title_full | New Hybrid Method for Buffer Positioning and Production Control Using DDMRP Logic in Smart Manufacturing |
| title_fullStr | New Hybrid Method for Buffer Positioning and Production Control Using DDMRP Logic in Smart Manufacturing |
| title_full_unstemmed | New Hybrid Method for Buffer Positioning and Production Control Using DDMRP Logic in Smart Manufacturing |
| title_short | New Hybrid Method for Buffer Positioning and Production Control Using DDMRP Logic in Smart Manufacturing |
| title_sort | new hybrid method for buffer positioning and production control using ddmrp logic in smart manufacturing |
| topic | Demand-Driven MRP (DDMRP) mixed-integer programming (MIP) Genetic Algorithm buffer positioning production planning reconfigurable manufacturing |
| url | https://www.mdpi.com/2504-4494/9/7/219 |
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