A Two-Dimensional Fabric Cutting Approach: Balancing Remnants and Overproduction

The cutting stock problem (CSP) is a critical optimization problem in the textile industry, where minimizing fabric waste and overproduction is essential for cost-effective production. This study proposes a methodology integrating a pattern generation algorithm and a multi-objective goal programming model for pattern selection. The proposed pattern generation algorithm creates guillotine cuts using a three-stage procedure, ensuring ease of applicability in manual production processes. The pattern selection model considers both fabric remnants and overproduction as waste, offering a comprehensive framework for the textile manufacturers. The multi-objective model is solved using both preemptive and weighted goal programming approaches, offering flexibility for aligning with varying order management strategies. A case study validates the effectiveness of the model in reducing overall waste while addressing key constraints in textile manufacturing. Compared to existing methods, the proposed approach enhances practicality and adaptability making it suitable for real-world textile production environments, particularly where manual cutting is employed.