A Methodology for Ship Cabin Equipment Layout Considering Human Factor Reliability Optimization

Qiaoyu Zhang, Hongshuo Zhang, Yan Lin, Yuansong Yang, Haiyang Liu
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Abstract

The paper proposes an innovative methodology of Human Cognitive Reliability- Cognitive Reliability and Error Analysis Method (HCR-CREAM) coupled with A* search and genetic algorithm (GA) to tackle ship cabin equipment layout considering human factor reliability optimization with the goal of minimizing human error probability (HEP) subjected to practical requirements. After establishing the mathematical model of cabin equipment inspection tasks in ship cabin equipment layout problem through HCR-CREAM and equipment geometric simplification, a method of the horizontal movement based on minimum distance is presented to avoid the equipment overlapping, then A* search is used for planning inspection paths and GA with selection, crossover, and mutation operators is applied to solve equipment layout results. A case of equipment layout in a certain ship engine room has been taken to carry out parameter sampling experiments by Latin Hypercube for GA. The results show the solution effect of GA is less affected by its parameter variation. And through the comparison with the initial equipment layout, the indicators influencing the HEP of the optimized result have been improved, thus significantly reducing HEP. ship cabin equipment layout; human factor reliability optimization; HCRCREAM; A* search; genetic algorithm
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考虑人为因素可靠性优化的船舱设备布局方法论
本文提出了一种创新方法--人的认知可靠性--认知可靠性和误差分析方法(HCR-CREAM),并结合 A* 搜索和遗传算法(GA),在考虑人的因素可靠性优化的前提下解决船舱设备布局问题,目标是使人为误差概率(HEP)最小,并符合实际要求。通过 HCR-CREAM 和设备几何简化建立了船舱设备布局问题中船舱设备检查任务的数学模型,提出了基于最小距离的水平移动方法以避免设备重叠,然后使用 A* 搜索规划检查路径,并应用带有选择、交叉和变异算子的 GA 求解设备布局结果。以某船舶机舱的设备布局为例,通过拉丁超立方对 GA 进行参数采样实验。结果表明,GA 的求解效果受参数变化的影响较小。通过与初始设备布局的对比,优化结果中影响 HEP 的指标得到了改善,从而显著降低了 HEP。 船舶机舱设备布局;人为因素可靠性优化;HCRCREAM;A*搜索;遗传算法
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