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  4. Vol 10, Issue 2, 2025

Research on Multi-Objective Optimisation-Based Task Package Division Methods for Shipbuilding

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DOI: 10.23977/jemm.2025.100205 | Downloads: 2 | Views: 18

Author(s)

Lijun Liu 1, Fei Ren 1, Jiahao Liu 1, Huisong Meng 1, Zuhua Jiang 2

Affiliation(s)

1 College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an, Shaanxi, China
2 School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

Corresponding Author

Lijun Liu

ABSTRACT

Shipbuilding requires converting multi-disciplinary product units—such as hull structures, outfitting equipment, and coating processes—into executable task packages to support sectional construction and final assembly on the slipway. Three critical issues arise during practical division: firstly, weak inter-process connectivity within task packages, where splitting welding and assembly tasks within the same compartment disrupts workflow continuity and increases redundant handling costs; Secondly, high coupling between task packages arises when construction packages are empirically divided, placing sequentially dependent tasks under different crews and triggering frequent cross-departmental coordination. Thirdly, resource allocation imbalances occur when critical resources like machining equipment are concentrated or scarce, constraining the construction cycle. Therefore, this paper constructs a multi-objective optimisation mixed-integer programming model. First, leveraging information entropy theory, we quantify the homogeneity of shipbuilding processes within task packages—such as hull welding and piping pre-installation—to ensure process continuity and specialised concentration. Second, a task dependency matrix quantitatively assesses and reduces inter-package coupling, minimising cross-package coordination costs. Finally, an equitable resource allocation metric is introduced, using variance in critical resource utilisation to achieve balanced distribution. Simultaneously, linearisation techniques address non-linear constraints, while a two-stage solution strategy combining branch-and-bound with genetic algorithms balances accuracy and efficiency. Ultimately, through case validation at a major Chinese shipbuilding enterprise, the proposed task package segmentation methodology demonstrated both its efficacy and practical feasibility. Empirical results demonstrate that this approach offers significant advantages in practical application. It enhances the cohesion of similar processes within task packages, effectively reduces the frequency of cross-package task coordination, and reasonably controls fluctuations in critical resource utilisation. Furthermore, it adapts to the complex scenarios of multi-disciplinary and multi-resource shipbuilding, providing a scientific decision-making tool for project task organisation and resource allocation.

KEYWORDS

Shipbuilding; Multi-Objective Optimisation; Engineering Decomposition; Task Package; Genetic Algorithm

CITE THIS PAPER

Lijun Liu, Fei Ren, Jiahao Liu, Huisong Meng, Zuhua Jiang, Research on Multi-Objective Optimisation-Based Task Package Division Methods for Shipbuilding. Journal of Engineering Mechanics and Machinery (2025) Vol. 10: 33-47. DOI: http://dx.doi.org/10.23977/jemm.2025.100205.

REFERENCES

[1] Cheng J, Ming J, Chen X. Distribution network reliability assessment and optimization method based on convolutional neural network[J].Engineering Research Express,2025,7(4):045339.DOI:10.1088/2631-8695/AE0F08.
[2] Takahashi S, Sasaki M, Takeda K, et al. Investigating fine- and coarse-grained structural correspondences between deep neural networks and human object image similarity judgments using unsupervised alignment[J].Neural Networks, 2026, 195108222.DOI:10.1016/J.NEUNET.2025.108222.
[3] Yong S, Lianlong G, Lianjie L, et al. An inverse model control method based on signal filtering and BP neural network to improve performance of electric servo cylinder system[J].Proceedings of the Institution of Mechanical Engineers, 2025, 239(22):9073-9087.DOI:10.1177/09544062251359409.
[4] Sewioło M, Mystkowski A. Agriculture Machine fault detection based on multiple Input-Parallel-Neural network optimized by genetic algorithm with matrix chromosomes[J].Computers and Electronics in Agriculture, 2025, 239(PC): 111125.DOI:10.1016/J.COMPAG.2025.111125.
[5] Kenanda M, Hammadi F, Bahai H, et al. A new efficient nonlocal hyperbolic HSDT for mechanical vibration of porous FGM plates/nanoplates using Navier’s method and artificial neural network prediction[J].International Journal of Solids and Structures,2026,325113719.DOI:10.1016/J.IJSOLSTR.2025.113719.
[6] Najamuddin, Sheikh U U, Sha’ameri Z A. Marine vessels acoustic classification with enhanced machinery and propeller feature extraction, using convolutional neural network[J].Measurement,2026,257(PA):118494. DOI: 10. 1016/J.MEASUREMENT.2025.118494.
[7] Li C, Zhao X, Lin L, et al. An evolutionary knowledge training-based proximal policy optimization algorithm for job shop scheduling in flexible intelligent manufacturing[J].Computers & Industrial Engineering,2025,210111533. DOI: 10.1016/J.CIE.2025.111533.
[8] Tan D, Yan L, Zhao J, et al. A black-box attack method of machine learning algorithms based on quantum autoencoders[J]. Physica A: Statistical Mechanics and its Applications,2025,680131033.DOI:10.1016/J. PHYSA. 2025.131033. 
[9] Ai W, Jiang Y, Pan X, et al. Neural network control of robotic manipulators with full-state time-varying constraints and composite disturbance observer[J].Journal of the Franklin Institute,2025,362(17):108084.DOI:10. 1016/J. JFRANKLIN. 2025.108084. 
[10] Liang J, Sun Z, Kang J, et al. Highly efficient Coordinate Measuring Machine error compensation via Greedy Randomized Kaczmarz algorithm and nongeometric error identification neural network[J]. Measurement, 2026, 258(PB): 119091.DOI:10.1016/J.MEASUREMENT.2025.119091. 
[11] Zhuang H, Lu W, Shen Q, et al. Off-policy reinforcement learning control for space manipulators based on object detection via convolutional neural networks[J].Aerospace Science and Technology,2026,168(PD):110914.DOI: 10.1016/J.AST.2025.110914. 
[12] Darwhekar M,Devi P Y,Murty N T, et al.Automated hydride segmentation and quantification in zirconium alloys using a convolutional neural network-assisted machine learning model[J].Journal of Nuclear Materials, 2026, 618156209.DOI:10.1016/J.JNUCMAT.2025.156209. 
[13] Ali H I,Alqahtani F M,Eljack D N, et al.Experimental and Machine Learning Modelling of Ni(II) Ion Adsorption onto Guar Gum: Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) Comparative Study[J]. Polymers, 2025, 17(20):2791.DOI:10.3390/POLYM17202791.
[14] Xu Y X,Wu C G,Xie D.A new discrete fractional AMAR model for finance time series forecasting by machine learning[J].Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena,2025,201(P2):117296.DOI:10.1016/J.CHAOS.2025.117296. 
[15] Chen Q,Sabir Z,Mehmood A M, et al.A machine learning radial basis deep neural network for solving the fractional chaotic financial system[J].Journal of Computational and Applied Mathematics,2026,474116936.DOI: 10. 1016/J. CAM.2025.116936. 
[16] Wang R,Lyu Z,Yan F, et al.A Conditional Adaption Alignment Dynamic Graph Neural Network model for unsupervised fault diagnosis of rotating machinery[J].Mechanical Systems and Signal Processing,2025,240113361. DOI:10.1016/J.YMSSP.2025.113361. 
[17] Yao W,Wang Y,Xiong L, et al.Dynamics of asteroidal Hopfield neural network under electromagnetic radiation and its application in the mechanical optimization design[J].Nonlinear Dynamics,2025,(prepublish):1-18.DOI:10. 1007/S11071-025-11846-1. 
[18] Pawlik L,Jakubowski W L J,Frej D, et al.Applications of Computational Mechanics Methods Combined with Machine Learning and Neural Networks: A Systematic Review (2015–2025)[J].Applied Sciences,2025,15(19):10816. DOI:10.3390/APP151910816. 
[19] Fadzail F N,Zali M S,Mid C E, et al.Hyperparameter optimization using automated machine learning based artificial neural network for fault detection in wind turbine generator[J].Next Research,2025,2(4):100785.DOI: 10. 1016/J.NEXRES.2025.100785. 
[20] Moghadamnejad A,Moghaddasi A M,Hamidia M, et al.Ranking Earthquake Prediction Algorithms: A Comprehensive Review of Machine Learning and Deep Learning Methods[J].Soil Dynamics and Earthquake Engineering,2026,200(PA):109740.DOI:10.1016/J.SOILDYN.2025.109740. 
[21] Hasan R S,Islam S M,Awal I Z, et al.Prediction and optimization of efficient ship design particulars through advanced machine learning approaches[J].Ocean Engineering,2025,341(P2):122572.DOI:10.1016/J. OCEANENG. 2025.122572. 
[22] Li H,Zeng Z,Li X, et al.Graph-convolutional neural networks for predicting tunnel boring machine performance[J]. Automation in Construction,2025,180106436.DOI:10.1016/J.AUTCON.2025.106436. 
[23] Huang Y,Tian J.Motion path optimization of truss manipulator based on simulated annealing and BP neural network[J]. Frontiers in Mechanical Engineering,2025,111643848.DOI:10.3389/FMECH.2025.1643848. 
[24] Petrov A P.An Overview of Forming Problems Solved with Machine and Deep Learning[J].Steel in Translation, 2025, 55(5):451-458.DOI:10.3103/S0967091225700925. 
[25] Arshad Z,Hussain K S,Othman A N, et al.Application of machine learning for numerical analysis of mixed convective Falkner–Skan flow of cross nanofluid through predictive neural networks algorithm[J].International Journal of Geometric Methods in Modern Physics,2025,(prepublish):DOI:10.1142/S0219887825502688. 
[26] Houssein H.An artificial neural network for predicting the roles of contacting bodies in computational contact mechanics[J].Neural Computing and Applications,2025,(prepublish):1-23.DOI:10.1007/S00521-025-11654-Z. 

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