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Citation:
　Sari Wanda Rulita,Gunawan,Muzhoffar Dimas Angga Fakhri.Optimization of Nesting Systems in Shipbuilding: A Review[J].Journal of Marine Science and Application,2025,(1):152-175.[doi:10.1007/s11804-025-00644-1]
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Optimization of Nesting Systems in Shipbuilding: A Review

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Title:
Optimization of Nesting Systems in Shipbuilding: A Review
Author(s):
Sari Wanda Rulita;  Gunawan;  Muzhoffar Dimas Angga Fakhri
Affilations:
Author(s):
Sari Wanda Rulita;  Gunawan;  Muzhoffar Dimas Angga Fakhri
Department of Mechanical Engineering, Universitas Indonesia, Kampus UI, Depok 16424, Indonesia
Keywords:
Cutting plate; Nesting algorithms; Nesting optimization; Shipbuilding efficiency; Algorithmic optimization
分类号:
-
DOI:
10.1007/s11804-025-00644-1
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.

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Memo

Memo:
Received date:2024-4-24;Accepted date:2024-10-14。
Foundation item:The authors would like to thank the Maritime Technology and Resources Study Program, Department of Mechanical Engineering, Universitas Indonesia, for supporting this research and the Indonesia Endowment Funds for Education or Lembaga Pengelola Dana Pendidikan (LPDP) from the Ministry of Finance for the funding support towards this article review under scholarship contract number 0000559/TRP/M/19/lpdp2023.
Corresponding author:Gunawan,E-mail:gunawan_kapal@eng.ui.ac.id
Last Update: 2025-02-26