基于两阶段多目标智能设计方法的船舶动力舱设备布局优化研究

Research on equipment layout optimization of ship engine room based on two-stage multi-objective intelligent design method

  • 摘要:
    目的 针对现有优化算法处理船舶动力舱设备布局优化问题时可行解占比低、收敛困难的状况,开展多目标智能设计方法研究,旨在实现智能化布局设计。
    方法 提出两阶段多目标优化方法。阶段1,以设备布置顺序为变量,基于 NSGA - II 算法与混合装箱算法,求解整数规划问题筛选初始布置方案。其中,混合装箱算法融合货架和天际线算法思路,优化目标包括空间利用率、通道及维修空间、维检修效率,约束条件涵盖设备干涉、维修可达、互斥、重心等方面。阶段2,以初始方案为基础,以设备间隔、通道宽度为变量优化得到最佳布局。
    结果 将该方法应用于某船舶动力舱局部区域设备布置,所得方案的维检修效率提升 17.18%,通道最大宽度及维修空间优化 0.47%,剩余有效空间利用率提高 33.36%,各项优化目标均不低于人工布置方案。通过参数实验进一步验证了 NSGA - II 算法参数、精英策略、网格参数的合理性及方法的通用性。
    结论 研究表明,两阶段优化方法可行且适用,能有效提高动力舱设备布置优化效率与效果,可为智能化布局设计提供解决方案。

     

    Abstract:
    Objective Aiming at the low proportion of feasible solutions and convergence difficulties in existing optimization algorithms for ship engine room layout optimization problems, this study conducts research on multi-objective intelligent design methods to achieve the intelligent layout design of the engine room.
    Methods A two-stage multi-objective optimization method is proposed. In the first stage, the order of the equipment layout is used as the variable and the initial layout scheme is screened by solving the integer programming problem based on the non-dominated sorting genetic algorithm-II (NSGA-II) algorithm and mixed packing algorithm. The mixed packing algorithm integrates the ideas of shelf and skyline algorithms, with optimization objectives including space utilization rate, aisle and maintenance space, and maintenance efficiency, and constraints covering equipment interference, maintenance accessibility, exclusivity, and center of gravity, among others. In the second stage, based on the initial scheme, the best layout is optimized with equipment spacing and aisle width as variables.
    Results Applying this method to the optimization of equipment layout in a local area of a ship's engine room, maintenance efficiency is increased by 17.18%, the maximum width of aisles and maintenance space is optimized by 0.47%, and the overall space utilization rate is significantly increased by 33.36%, with all optimization objectives not lower than the manual layout schemes. At the same time, parameter experiments verify the rationality of the NSGA-II algorithm parameters, elite strategies, grid parameters, and generality of the method.
    Conclusions The two-stage optimization method is feasible and applicable, effectively improving the efficiency and effectiveness of engine room layout optimization, and providing a solution for intelligent layout design.

     

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