Simulation-based severe weather-induced container terminal economic loss estimation

Container terminals play a critical role in maritime supply chains. However, they show vulnerabilities to severe weather events due to the sea–land interface locations. Previous severe weather risk analysis focused more on larger assessment units, such as regions and cities. Limited studies assessed severe weather risks on a smaller scale of seaports. This paper aims to propose a severe weather-induced container terminal loss estimation framework. Based on a container terminal operation simulation model, monthly average loss and single event-induced loss are obtained by using historical hazard records and terminal operation records as model inputs. By studying the Port of Shenzhen as the case study, we find that the fog events in March lead to the longest monthly port downtime and the highest monthly severe weather-induced economic losses in the studied port. The monthly average loss is estimated to be 30 million USD, accounting for 20% of the intact income. The worst-case scenario is found to be a red-signal typhoon attack which results in nearly 20% decrease in the month’s income. The results provide useful references for various container terminal stakeholders in severe weather risk management.     

尺寸可变的装箱问题的近似算法的研究

给定物品系列,要求将所有物品装入到不同类型的箱子中,以实现从第1个箱子到最后1个箱子被使用的箱子的总尺寸最小化.用最坏情况绝对性能研究在线算法,给出了一种最坏情况绝对性能比是3的近似算法.作为这种算法的应用,给出了一种脱线算法,其最坏情况绝对性能比是2.     

基于CVaR的海运集装箱超订模型

将条件风险价值(CVaR)度量准则应用于集装箱舱位超订的风险管理研究.建立了在CVaR准则下的海运集装箱舱位超订模型,解此模型得到舱位最优超订水平需满足的方程组,并讨论了风险厌恶程度和空箱调运对舱位最优超订水平的影响.分析结果表明,在风险厌恶环境中,集装箱舱位的最优超订水平依赖于需求的分布和风险厌恶程度,不一定小于风险中性时的最优超订水平;与不考虑空箱调运时比较,考虑空箱调运时,最优超订水平会降低.     

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在基于走行时间可靠性的交通均衡问题中,普遍存在假设是引起走行时间变异的O D (Origin Destination)需求或路段通行能力的概率分布是精确已知的。然而,现实中这些概率分布很难精确获得.本文放松这个假设而仅要求知道O D需求的前m阶矩(这里m是和路段费用函数的形式相关的正整数),通过运用最坏风险价值和最坏条件风险价值指标定义鲁棒分位走行时间和鲁棒超过期望走行时间,并证明在一般分布下两种出行时间是等价的.基于此定义,通过整合出行者的感知误差,提出了鲁棒分位随机用户均衡(鲁棒超过期望随机交通均衡)模型,模型被表示为一个变分不等式,并证明了解的存在性,然后运用一种启发式算法求解该模型.数值算例显现了模型在应用上的特性及算法上的有效性.     

Optimal portfolio choice for ship leasing investments

ABSTRACT

We develop an advanced portfolio analysis method to determine the optimal ship mix for a portfolio. A typical business model of a ship leasing firm is to acquire ships for charter income. However, transportation assets (such as ships and aircraft) are different from financial assets (e.g. stocks and bonds) as they are tangible and have a limited useful life. Thus, methodologies for financial portfolios cannot be simply extended to portfolios of transportation assets. Recently, ship leasing represents a non-traditional source of ship finance. A ship leasing firm can manage a certain number of ships across different ship types. Based on portfolio theory and industry-related evidence, we construct an optimal ship portfolio consisting of a manageable combination of carefully chosen ships. The expected return of the asset combination is examined with respect to various ship types and sizes. We quantify the freight risk for different categories of ships in different sectors to investigate the characteristics of ship portfolios. The estimation of such freight risk is of considerable interest to academics and practitioners alike, because determining the behaviour of freight risk has important implications for investment decisions regarding ships. Our portfolio method should strongly support the decision-making process involved in leasing ships.     

基于条件风险价值的投资组合优化模型

采用R T Rockafellar和S Uryasev的一种优化算法，构造了一个以条件风险价值代替标准差度量风险的投资组合优化模型．选择沪、深股市6种股票构成一个投资组合，用Matlab软体对模型进行优化计算，得到了该投资组合的有效前沿和投资权重，并与用传统的均值方差模型的计算结果进行了比较．结果表明，这2个模型优化得到的有效前沿非常相近，与国外研究获得的有效前沿图形也非常相似，但这2个模型优化得到的投资权重却有较大差异．