A knowledge discovery and reuse method for time estimation in ship block manufacturing planning using DEA

Li, Jinghua; Sun, Miaomiao; Han, Duanfeng; Wang, Jiaxuan; Mao, Xuezhang; Wu, Xiaoyuan

doi:10.1016/j.aei.2018.11.005

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…Data mining adalah proses pengolahan data dengan menggunakan algoritma yang mengikuti alur data mining. Algoritma K-Means Clustering digunakan dalam penelitian ini (Li et al, 2019).…”

Section: Data Miningunclassified

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Pengelompokan Responsivitas Hasil Kuesioner terhadap Penggunaan Perpustakaan Digital Menggunakan Algoritma K-Means

Kusuma,

Kurniawan

2024

JIP

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Perpustakaan Digital telah menjadi bagian integral dalam pelayanan informasi di lembaga pendidikan tinggi, belum diketahuinya tingkat responsivitas Perpustakaan Digital menjadi masalah terkait karena sulit untuk menentukan dan mengidentifikasi evaluasi dan strategi perbaikan kedepan untuk memahami persepsi pengguna terhadap layanan yang disediakan. Penelitian ini bertujuan untuk mengidentifikasi dan mengelompokkan tingkat responsif pengguna terhadap layanan perpustakaan digital di STMIK IKMI Cirebon. Metode pengelompokan yang digunakan adalah algoritma K-Means untuk menganalisis hasil kuesioner yang mengevaluasi persepsi dan kepuasan pengguna terhadap berbagai aspek perpustakaan digital. Kuesioner dirancang tiga (3) parameter utama yang terdiri dari efektivitas, efisiensi, dan tingkat kepuasan pengguna (user experience). Hasil Clustering menunjukan Tiga kelompok utama, yaitu Cluster P1, P2, dan P3, diidentifikasi berdasarkan atribut yang berbeda. Cluster P1, dengan atribut X1, X2, dan X3, menunjukkan nilai jarak rata-rata antar centroid yang rendah (1.101) dan Davies Bouldin Index (DBI) yang rendah (0.841), menandakan tingkat kohesi dan pemisahan yang baik di antara anggota kelompok ini. Sementara Cluster P2 (dengan atribut Y1, Y2, Y3, dan Y4) menunjukkan nilai jarak antar centroid yang lebih tinggi (2.670) dan DBI yang lebih tinggi (1.285). mengindikasikan adanya sejumlah overlapping antar cluster atau pemisahan yang kurang optimal, terutama bila dibandingkan dengan Cluster P1.

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“…Data mining adalah proses pengolahan data dengan menggunakan algoritma yang mengikuti alur data mining. Algoritma K-Means Clustering digunakan dalam penelitian ini (Li et al, 2019).…”

Section: Data Miningunclassified

“…Langkah Knowledge ini menyajikan data yang dibuat oleh proses pengelompokan; fase ini memberikan hasil dalam format yang dapat dimengerti (Li et al, 2019).…”

Section: Knowledgeunclassified

Pengelompokan Responsivitas Hasil Kuesioner terhadap Penggunaan Perpustakaan Digital Menggunakan Algoritma K-Means

Kusuma,

Kurniawan

2024

JIP

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“…Based on the previous study [42], the measured data access and the forecasted data output are additional elements in the work hours forecasting framework, as shown in Figure 8. The measured data from the data acquisition layer of the DT framework is input to the work hours quota database and the process route database, and K-means-based clustering analysis of welding processes is performed according to the operational characteristics to evaluate the work hours quotas according to the classification of different types of welding processes with various difficulties.…”

Section: Work Hours Forecastingmentioning

confidence: 99%

Modeling of Digital Twin Workshop in Planning via a Graph Neural Network: The Case of an Ocean Engineering Manufacturing Intelligent Workshop

Li,

Yin,

Yang

et al. 2023

Applied Sciences

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In the era of Industry 4.0 to 5.0, the manufacturing industry is dedicated to improving its production efficiency, control capability and competitiveness with intelligent enhancement. As a typical discrete manufacturing industry, it is difficult for ocean engineering (OE) manufacturers to accurately control the entire production process, and the establishment of an integrated system supported by digital twin (DT) technology is a better solution. This paper proposes a comprehensive set of system architectures for the DT workshop. It focuses on planning, which is the main line of control, to establish a model based on graph neural networks (GNNs) and suggests five decision-support approaches associated with the model from a practical application perspective. The utilization of complete twin data for prediction and visual simulation effectively eliminates the problem of unexpected factors interfering with scheduling in enterprise production planning and achieves the goals of rapid processing and just-in-time completion. The planning model is based on the attention mechanism, which characterizes the disjunctive graph, extracts the input GNN, and outputs the scheduling decision by constructing the multi-attention network of operations and machines to deal with the complicated “operation–machine” combination relationship. The proposed method has been verified in the case of structural assembly and welding workshops, has validity and reliability, and is superior to the traditional priority scheduling rules and heuristics in terms of precision rate and rapidity. Furthermore, the DT system completes the production line application, and its proven reliability supports its full-scale application in future smart factories.

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“…Mao et al (2020) presented an agent-based framework for collaborative planning and scheduling in shipbuilding projects. Other studies focused on improving time estimation precision(Li et al, 2019), shipbuilding block assembly line scheduling(Cho et al, 2022), and large-scale shipyard scheduling problems(Han et al, 2017). Jeong et al(2018) proposed efficient spatial arrangement planning for shipyards, while Ge & Wang (2021) addressed block spatial scheduling optimization and scheduling strategies for irregular curved blocks.…”

mentioning

confidence: 99%

Optimizing Shipbuilding Production Project Scheduling Under Resource Constraints Using Genetic Algorithms and Fuzzy Sets

AKAN,

ALKAN

2023

Marine Science and Technology Bulletin

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This study explores the application of Genetic Algorithms (GA) in optimizing shipbuilding production processes in the presence of uncertain environments. The research addresses two key aspects: firstly, the integration of GA RCPSP (Resource-Constrained Project Scheduling Problem) with techniques for managing uncertainty in shipbuilding production; and secondly, the analysis of Pareto optimal solutions generated by GA to achieve optimal scheduling in the shipbuilding context. The proposed framework aims to minimize project completion time and maximize resource utilization by incorporating probabilistic models, scenario analysis to handle uncertainties. Furthermore, the study focuses on evaluating the trade-offs between project completion time, resource allocation, and cost through the analysis of Pareto optimal solutions, using visualization techniques and sensitivity analyses to support decision-making processes. The findings contribute to enhancing shipbuilding production by providing a comprehensive approach for effectively managing uncertainty, improving resource allocation, and reducing project duration through the integration of GA RCPSP and uncertainty management techniques.

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A knowledge discovery and reuse method for time estimation in ship block manufacturing planning using DEA

Cited by 9 publications

References 26 publications

Pengelompokan Responsivitas Hasil Kuesioner terhadap Penggunaan Perpustakaan Digital Menggunakan Algoritma K-Means

Pengelompokan Responsivitas Hasil Kuesioner terhadap Penggunaan Perpustakaan Digital Menggunakan Algoritma K-Means

Modeling of Digital Twin Workshop in Planning via a Graph Neural Network: The Case of an Ocean Engineering Manufacturing Intelligent Workshop

Optimizing Shipbuilding Production Project Scheduling Under Resource Constraints Using Genetic Algorithms and Fuzzy Sets

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