摘要
综合安全评估(FSA)在国际海事组织(IMO)规则制定过程中的潜力已逐步得到认同,国内已将FSA列入长期跟踪和研究的IMO热点问题之一。论文针对FSA发展中面临的一些问题进行了研究,发现FSA研究具有对专家评价依赖度高、历史数据不完善、模型有待健全、评估方法存在一定的不确定性、评估依据的充分性有待提高、过程脱节有待改善等特点,重点研究了专家评价质量的度量方法、人为失误、专家语义评价聚合方法及多指标风险控制方案选择方法等问题,提出了解决问题的思路、方法和模型。
针对专家评价度量方法,认为专家评价具有主观、模糊和不确定性,提出应从复现性和准确性角度进行度量。论文使用了传递熵对专家评价的不确定性进行度量,提出了改善评价一致度的方法。使用了传递熵模型和欧式距离矩阵模型对专家赋权,构造了离差函数进行专家权重聚合,建立了针对性强的专家评价评定及客观赋权模型。
就缺乏人为失误历史数据及有效的定量分析模型问题,论文使用专家问卷调查法获得了有关人为失误的发生频率。为确定人为失误的发生概率,论文将专家的语义评价转换成梯形模糊数。专家个人背景和资历对主观评价具有影响,为体现这一特点,论文使用了全局快速K-均值聚类算法对专家进行聚类,并利用复现性和准确性关系理论,以各簇心为基础,以最优专家为参照,利用欧式距离矩阵模型确定以簇为单位的专家整体权重。为了对簇内专家语义评价结果进行聚合,基于专家评价的模糊性,使用了模糊相似度分析模型确定簇内各专家语义评价的模糊权重。在此基础上,先对专家语义评价实施簇内聚合,再簇间聚合以获得最终的专家语义评价梯形模糊数,建立了基于聚类算法和模糊相似度模型的系统的专家赋权模型,提高了专家赋权的客观性。同时借助于经验公式确定人为失误的发生概率。
提出了基于优势度的风险控制方案选择模型。为体现海上安全政策制定过程中管理者主观决策的倾向性,借鉴层次分析法的思想,通过对FSA专家的问卷调查,获得了专家对风险控制方案选择指标重要性的认识,并利用一定的算法得到了各指标的权重,在此基础上利用模型计算了各风险控制方案的优势度。模型不但充分利用了样本在各指标上的序关系,而且还考虑了差异程度,解决了FSA研究中风险控制方案选择过程中主观因素强的不足。模型给出的计算结果具有直观、明显的特点,利于决策者决策。研究发现在双指标风险控制方案排序中,总灾难转移费作为首选参考指标的筛选作用明显；但在多指标风险控制方案排序中,总灾难转移费作为首选参考指标的筛选作用不明显,需要借助于模型提高筛选效果。
The potential of Formal Safety Assessment (FSA) in International Maritime Organization (IMO) rulemaking process has been gradually acknowledged, and China lists FSA as one of the IMO hot issues worth long-term tracing and researching. This dissertation studies some problems confronting FSA development and finds certain features of FSA study, such as high dependence on experts, imperfect historical data, incomplete model, uncertainty in evaluation methods, inadequacy of evaluation bases, and discrepancies to be eliminated in process, etc. The focus is on the studies of the measurements exploited by experts in evaluating quality, of human errors, of polymerization of experts'semantic evaluation, and of selection methodologies concerning multi-index risk control options (RCOs). Ideas, methodologies, and models of solving the problems are also proposed.
Concerning expert evaluation measurements, it is proposed that measures should be conducted in respects of repeatability and accuracy owing to the subjectivity, obscurity, and uncertainty of expert evaluation. The dissertation measures the uncertainty of expert evaluation employing transference entropy, and proposes a method of improving degree of evaluation agreement. It also obtains experts'weights using transference entropy model and Euclidean distance matrix model, aggregates experts' weights with the construction of dispersion functions, and establishes well-targeted models of expert evaluation assessment and objective weight obtaining.
With regard to the problem of lacking historical data of human errors and effective models for quantitative analysis, the dissertation obtains frequency of pertinent human errors using expert questionnaires. Experts'semantic evaluation is converted to trapezoidal fuzzy numbers to determine the probability of occurrence of human errors. The dissertation clusters experts, using a global quick K-means clustering algorithm, in order to demonstrate the feature that subjective evaluation is influenced by experts' personal background and qualifications. Experts'integral weights in terms of clusters are determined with Euclidean distance matrix model, according to the theory of relations between reproducibility and accuracy, based on every cluster center, and with reference to the optimal expert. Owing to the obscurity of expert evaluation, the obscure weights of in-cluster experts'semantic evaluation are determined with fuzzy similarity analysis model to aggregate the results of in-cluster experts'semantic evaluation. On the above basis, in-cluster aggregation followed by inter-cluster aggregation of experts' semantic evaluation is executed to obtain the ultimate trapezoidal fuzzy numbers of experts'semantic evaluation. A systematic expert weight obtaining model is established based on clustering algorithms and fuzzy similarity models, increasing the objectivity of expert weight obtaining. Meanwhile, empirical formulas are employed to determine the probability of occurrence of human errors.
A selection model of RCO based on dominance degree is proposed. Experts' understanding of the importance of RCO selection indexes is acquired through FSA experts' questionnaires to represent decision makers'subjective decision orientation in the process of maritime security policy institution, with reference to analytic hierarchy process (AHP). Weights of every index are gained with certain algorithms, and the dominance degree of each RCO is calculated via the model on this basis. The model not only makes full use of samples'order relationship on each index, but also takes into consideration the degree of difference, thus overcomes the disadvantage of strong subjective factors in the process of RCO selection in FSA studies. The model's calculation results are direct and clear, facilitating decision making. It is found that GCAF as the preferred reference index has apparent screening effect in dual-index RCO ranking, but unapparent screening effect in multi-index RCO ranking which requires the model to improve screening effect.
引文
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