Integrated Berth Allocation and Quay Crane Assignment Problem: Set partitioning models and computational results

Most of the operational problems in container terminals are strongly interconnected. In this paper, we study the integrated Berth Allocation and Quay Crane Assignment Problem in seaport container terminals. We will extend the current state-of-the-art by proposing novel set partitioning models. To improve the performance of the set partitioning formulations, a number of variable reduction techniques are proposed. Furthermore, we analyze the effects of different discretization schemes and the impact of using a time-variant/invariant quay crane allocation policy. Computational experiments show that the proposed models significantly improve the benchmark solutions of the current state-of-art optimal approaches.     

A heuristic approach for solving an integrated gate reassignment and taxi scheduling problem

Capacity limitation of airport ground operation is one of the major limiting factors in air traffic operation. The congestion on the gate and taxiway causes severe delay and propagate effect on the flight schedule. This paper considers the problem of integrated gate reassignment and taxiway scheduling, in which complex constraints related to runway restriction, gate allocation and taxiway conflict are all incorporated when determining the schedule. To solve this problem, we propose a novel heuristic approach. First, all possible aircraft schedules are enumerated by disretizing the waiting time along the path. Then, the cost is evaluated for each schedule and the conflict detection is conducted to generate constraint sets. Finally, we propose a set partition model, in which each decision variable denotes a candidate schedule that takes into account the possible constraints when generated. This method is compared with a sequential method that solves gate reassignment and taxiway scheduling problem separately. Computational results highlight the strength of our method.     

浅谈事故树在铁路调车脱轨事故分析中的应用

为降低铁路调车脱轨事故的发生概率,通过搜集汇总常见的调车脱轨事故案例,归类分析查找事故致因。采用事故树分析法,对调车脱轨的影响因素进行结构建模。利用布尔代数原理对事故树结构函数式进行化简,求出事故树的最小割集和最小径集,对影响因素进行结构重要度分析,得出影响调车脱轨事故的关键因素。最后,结合最小割集和最小径集的组成事件及其关联程度,分析制定预防调车脱轨事故的主要措施,即吸取典型事故教训、强化设备用、管、修制度执行、严格执行调车作业标准、做好调车脱轨事故应急处置,为铁路调车安全管理和决策提供参考依据。     

Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model

The rough Bergomi (rBergomi) model, introduced recently in Bayer et al. [Pricing under rough volatility. Quant. Finance, 2016, 16(6), 887–904], is a promising rough volatility model in quantitative finance. It is a parsimonious model depending on only three parameters, and yet remarkably fits empirical implied volatility surfaces. In the absence of analytical European option pricing methods for the model, and due to the non-Markovian nature of the fractional driver, the prevalent option is to use the Monte Carlo (MC) simulation for pricing. Despite recent advances in the MC method in this context, pricing under the rBergomi model is still a time-consuming task. To overcome this issue, we have designed a novel, hierarchical approach, based on: (i) adaptive sparse grids quadrature (ASGQ), and (ii) quasi-Monte Carlo (QMC). Both techniques are coupled with a Brownian bridge construction and a Richardson extrapolation on the weak error. By uncovering the available regularity, our hierarchical methods demonstrate substantial computational gains with respect to the standard MC method. They reach a sufficiently small relative error tolerance in the price estimates across different parameter constellations, even for very small values of the Hurst parameter. Our work opens a new research direction in this field, i.e. to investigate the performance of methods other than Monte Carlo for pricing and calibrating under the rBergomi model.     

The effects of exposure-order and market entry-information on brand preference: a dual process model

A dual process model is proposed that identifies the conditions under which two separate and additive effects on brand preference may obtain. The first is an exposure-order effect that can influence attribute-based processing. The second is the effect of market entry-information that can result in theory-driven processing. The model was tested and supported across two experiments, which demonstrate that the effects of market entry-information and exposure-order on brand preference depend on attribute-type and the time between attribute encoding and preference construction. In contrast to previous research, conditions are identified where market entry-information has no effect on preference and where the second-encountered brand is preferred to the first.     

On VIX futures in the rough Bergomi model

The rough Bergomi model introduced by Bayer et al. [Quant. Finance, 2015, 1–18] has been outperforming conventional Markovian stochastic volatility models by reproducing implied volatility smiles in a very realistic manner, in particular for short maturities. We investigate here the dynamics of the VIX and the forward variance curve generated by this model, and develop efficient pricing algorithms for VIX futures and options. We further analyse the validity of the rough Bergomi model to jointly describe the VIX and the SPX, and present a joint calibration algorithm based on the hybrid scheme by Bennedsen et al. [Finance Stoch., forthcoming].     

基于有效集检验CAPM对中国股票市场的有效性

资本资产定价模型(CAPM)有效性的主要假设是市场组合位于全局均值-方差有效集上,有效集理论可用来对CAPM的有效性进行检验。本文利用沪深股市1991～2008年的数据对CAPM在中国股票市场的有效性进行了检验,综合检验的结果表明:中国股票市场趋于有效。     

Forest carbon benefits,costs and leakage effects of carbon reserve scenarios in the United States

This study evaluated the potential effectiveness of future carbon reserve scenarios, where U.S. forest landowners would hypothetically be paid to sequester carbon on their timberland and forego timber harvests for 100 years. Scenarios featured direct payments to landowners of $0 (baseline), $5, $10, or $15 per metric ton of additional forest carbon sequestered on the set aside lands, with maximum annual expenditures of $3 billion. Results indicated that from 1513 to 6837 Tg (Teragrams) of additional carbon (as carbon dioxide equivalent, CO₂e) would be sequestered on U.S. timberlands relative to the baseline case over the next 50 years (30–137 Tg CO₂e annually). These projected amounts of sequestered carbon on timberlands take into account projected increases in timber removal and forest carbon losses on other timberlands (carbon leakage effects). Net effectiveness of carbon reserve scenarios in terms of overall net gain in timberland carbon stocks from 2010 to 2060 ranged from 0.29 tCO₂e net carbon increase for a payment of $5/tCO₂e to the landowner (71% leakage), to 0.15 tCO₂e net carbon increase for a payment of $15/tCO₂e to the landowner (85% leakage). A policy or program to buy carbon credits from landowners would need to discount additions to the carbon reserve by the estimated amount of leakage. In the scenarios evaluated, the timber set-asides reduced timberland area available for harvest up to 35% and available timber inventory up to 55%, relative to the baseline scenario over the next 50 years, resulting in projected changes in timber prices, harvest levels, and forest product revenues for the forest products sector.     

TEACHING RESOURCE Using MONOPOLY TM and Teams-GamesTournaments in accounting education: a cooperative learning teaching resource

A three-week financial accounting simulation was conducted using university accounting majors (n=36) in one section of a junior-level financial reporting issues course. The curriculum involved using the board game Monopoly as a teaching resource in cooperative learning teams. Findings indicate students' attitudes toward financial accounting and learning, mutual concern for fellow students and perceived achievement were very positive upon completion of this cooperative learning exercise. Additionally, gender and student ability were found to impact the level of some measures of student attitudes and perceived achievement.     

Air itinerary choice in a low-frequency market: A decision rule approach

This paper focuses on low frequency airline service patronage using the data from a survey conducted at Imam Khomeini International Airport in Tehran, Iran. It uses Dominance-based Rough Set Approach (DRSA) for modeling passengers’ choices. We compare the method with discrete choice models using a prediction potential measure. Unlike discrete choice methods, it can use qualitative and categorized data without having to transform them into dummy variables. It is found that the prediction accuracy of the proposed technique is better than that of the discrete choice model for the case under consideration.