Optimization of Nesting Systems in Shipbuilding: A Review

Abstract

This review article provides a comprehensive analysis of nesting optimization algorithms in the shipbuilding industry, emphasizing their role in improving material utilization, minimizing waste, and enhancing production efficiency. The shipbuilding process involves the complex cutting and arrangement of steel plates, making the optimization of these operations vital for cost-effectiveness and sustainability. Nesting algorithms are broadly classified into four categories: exact, heuristic, metaheuristic, and hybrid. Exact algorithms ensure optimal solutions but are computationally demanding. In contrast, heuristic algorithms deliver quicker results using practical rules, although they may not consistently achieve optimal outcomes. Metaheuristic algorithms combine multiple heuristics to effectively explore solution spaces, striking a balance between solution quality and computational efficiency. Hybrid algorithms integrate the strengths of different approaches to further enhance performance. This review systematically assesses these algorithms using criteria such as material dimensions, part geometry, component layout, and computational efficiency. The findings highlight the significant potential of advanced nesting techniques to improve material utilization, reduce production costs, and promote sustainable practices in shipbuilding. By adopting suitable nesting solutions, shipbuilders can achieve greater efficiency, optimized resource management, and superior overall performance. Future research directions should focus on integrating machine learning and real-time adaptability to further enhance nesting algorithms, paving the way for smarter, more sustainable manufacturing practices in the shipbuilding industry.

RIGHTS & PERMISSIONS

Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature