New Hybrid Method for Buffer Positioning and Production Control Using DDMRP Logic in Smart Manufacturing

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Sahar Habbadi, Ismail El Mouayni, Brahim Herrou, Souhail Sekkat
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Journal of Manufacturing and Materials Processing
Subjects:
Demand-Driven MRP (DDMRP)
mixed-integer programming (MIP)
Genetic Algorithm
buffer positioning
production planning
reconfigurable manufacturing
Online Access:https://www.mdpi.com/2504-4494/9/7/219
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2504-4494

Similar Items