An optimisation framework for yard planning in a container terminal: case with automated rail-mounted gantry cranes

Different terminals, with their unique combinations of liner services, yard layouts and equipment configurations, may find that different yard planning strategies work better for their scenarios. While an optimum yard plan can be found for each yard planning strategy, it is interesting to know which strategy gives the best plan. In designing an IT-based search engine to discover the best yard planning strategy and/or scenario, having a generic specification and solver is important, so that the whole solution space could be represented and searched. We design a generic problem specification with parameterised scenarios and yard planning strategies, and formulate a generic mathematical model that solves for the optimum weekly yard plan template for that given problem. A good run time of this generic model is extremely important as the model will be executed hundreds of times in the search engine. Experiments are conducted with the model. An interesting discovery is that re-modelling a set of integer variables into multiple binary variables improve the run time tremendously, and in some cases, outperform the relaxed original model. We also find that the strategy which allows sharing of yard space between services yield better utilization for yard space and rail mounted gantry handling capacity.     

高性能交易引擎ZCETE的设计与实现

针对大规模电子交易中存在的数据密集和事务集中引起交易引擎的性能下降问题,结合内存数据库和主动数据库技术,提出并实现了一个高性能交易引擎ZCETE。讨论了ZCETE中采用的主动响应机制、事务调度策略、数据组织方法、故障恢复措施等关键技术。将ZCETE应用于棉花交易,测试结果表明,在不产生事务延迟的条件下,交易事务的处理速率为1970TPS,平均响应时间为0．5ms,可满足大规模电子交易的需要。     

An empirical analysis of risk-taking in car driving and other aspects of life

The link between risk-taking behavior in various aspects of life has long been an area of debate among economists and psychologists. Using an extensive data set from Denmark, this study provides an empirical investigation of the link between risky driving and risk taking in other aspects of life, including risk-taking behavior in financial and labor-market decisions. Specifically, we establish significant positive correlations between individuals’ risk-taking behavior in car driving and their risk-taking behavior in financial and labor-market decisions. However, we find that the strength of these correlations vary significantly between genders, and across risk decisions. These correlations and their differences across genders get stronger when we construct more “homogenous” groups by restricting our sample to those individuals with at least some stock-market participation. Overall, the empirical results in this study suggest that risk-taking behavior in various aspects of life can be associated, and our results corroborate previous evidence on the link between individuals’ risk preferences across various aspects of life. This implies that individuals’ driving behavior, which is commonly unobservable, can be more fully understood using observable labor market and financial decisions of individuals.     

The dynamic relation between financial positions and investment: evidence from company account data

A key feature of recent financial business cycle theories isthe existence of a two-way dynamic relation between financialfactors and investment: over time firms' financial positionsare affected by, and in turn affect, investment decisions. Thispaper investigates the dynamic interaction between financialconditions and investment by estimating and testing vector autoregressionson company account panel data for US manufacturing firms, whileconsidering explicitly sectional and time heterogeneity. Theresults show that indicators of liquidity and solvency containsignificant predictive information for investment at the firmlevel. We also find evidence of both cross-sectional and timeheterogeneity: the role played by financial factors is significantlymore important for highly leveraged than for low-debt firms;capital market frictions are shown to have asymmetric effectsover the business cycle, displaying a larger impact in contractionsthan in expansions. Overall, the evidence supports the hypothesisthat capital market imperfections have an important role inexplaining aggregate cyclical dynamics.     

基于用户体验的搜索引擎有效性评估研究

Forrester研究公司提供了5279名用户基于11个用户体验评价指标对4种搜索引擎进行有效性评价的结果,应用探索性因素分析法研究其内在影响因素。结果表明：①搜索引擎有效性的用户体验包含两个主要维度：内容准确性和内容直接性,是对11个评价指标的有效综合;文献综述结果表明,两个维度的用户体验具有相应的用户行为模式支持;②以内容准确性和内容直接性为评价指标对4种主流搜索引擎进行用户体验评估,评估结果与搜索引擎特征以及第三方评估结果相一致,并能有效解释搜索引擎特征与用户体验之间的关系。     

Maximization of a percentile life of a series system through component redundancy allocation

In a redundancy allocation problem, maximization of system reliability for a specified mission time has been thoroughly studied. Instead, we consider the optimal redundancy which maximizes a percentile life of a series system without violating a cost constraint. A percentile life is the maximum mission time for which system reliability meets at least a specific value. The proposed measure has advantages over regular reliability maximization in fixing warranties or when a system has no clear mission time. Previously, the proposed problem was solved by a heuristic or a genetic algorithm. Because of the infeasibility of finding a close form of percentile life in the redundancy level, we now develop a lexicographic search methodology to obtain an exact optimal solution. A transformed problem is first considered to get an upper bound, which is iteratively used to reduce search space. When any two stages of a system have a precedence relationship based on the cost and lifetime then the search space can be further reduced. The algorithm is general for any continuous increasing lifetime distributions, and can be easily extended for the additional weight and volume constraints.     

Detection and characterization of chemotaxis without cell tracking

Swarming has been observed in various biological systems from collective animal movements to immune cells. In the cellular context, swarming is driven by the secretion of chemotactic factors. Despite the critical role of chemotactic swarming, few methods to robustly identify and quantify this phenomenon exist. Here, we present a novel method for the analysis of time series of positional data generated from realizations of agent-based processes. We convert the positional data for each individual time point to a function measuring agent aggregation around a given area of interest, hence generating a functional time series. The functional time series, and a more easily visualized swarming metric of agent aggregation derived from these functions, provide useful information regarding the evolution of the underlying process over time. We extend our method to build upon the modelling of collective motility using drift–diffusion partial differential equations (PDEs). Using a functional linear model, we are able to use the functional time series to estimate the drift and diffusivity terms associated with the underlying PDE. By producing an accurate estimate for the drift coefficient, we can infer the strength and range of attraction or repulsion exerted on agents, as in chemotaxis. Our approach relies solely on using agent positional data. The spatial distribution of diffusing chemokines is not required, nor do individual agents need to be tracked over time. We demonstrate our approach using random walk simulations of chemotaxis and experiments investigating cytotoxic T cells interacting with tumouroids.     

Understanding safe performance in rapidly evolving systems: a risk management analysis of the 2010 US financial market Flash Crash with Rasmussen's risk management framework

Systemic events or widespread disruptions in financial markets like the Flash Crash are a public concern as they jeopardise investors’ confidence in financial markets and result in financial losses for market participants. Federal regulations for financial markets do not keep pace with the evolution and growth of financial technology including advances in algorithmic and high-frequency trading (HFT). We report an analysis of the Flash Crash which occurred on 6 May 2010 using Rasmussen's 1997 risk management framework. While the framework has been validated on a number of well-documented accidents, our work examines the framework in the context of a large-scale adverse event associated with complex technologies and automated systems capable of evolving over time. Our contribution is a set of implications of the Flash Crash associated with Rasmussen's propositions. These implications would inform regulators and risk assessment methods for rapidly evolving complex socio-technical systems.     

Tramp ship routing and scheduling with voyage separation requirements

In this paper we explore tramp ship routing and scheduling. Tramp ships operate much like taxies following the available demand. Tramp operators can determine some of their demand in advance by entering into long-term contracts and then try to maximise profits from optional voyages found in the spot market. Routing and scheduling a tramp fleet to best utilise fleet capacity according to current demand is therefore an ongoing and complicated problem. Here we add further complexity to the routing and scheduling problem by incorporating voyage separation requirements that enforce a minimum time spread between some voyages. The incorporation of these separation requirements helps balance the conflicting objectives of maximising profit for the tramp operator and minimising inventory costs for the charterer, since these costs increase if similar voyages are not performed with some separation in time. We have developed a new and exact branch-and-price procedure for this problem. We use a dynamic programming algorithm to generate columns and describe a time window branching scheme used to enforce the voyage separation requirements which we relax in the master problem. Computational results show that our algorithm in general finds optimal solutions very quickly and performs much faster compared to an earlier a priori path generation method. Finally, we compare our method to an earlier adaptive large neighbourhood search heuristic and find that on similar-sized instances our approach generally uses less time to find the optimal solution than the adaptive large neighbourhood search method uses to find a heuristic solution.

     

The search for life in our Solar System and the implications for science and society

The search for another type of life in the Solar System addresses the fundamental question of life in the Universe. To determine if life forms we discover represent a second genesis, we must find biological material that would allow us to compare that life to the Earth's phylogenetic tree of life. An organism would be alien if, and only if, it did not link to our tree of life. In our Solar System, the worlds of interest for a search for life are Mars, Europa, Enceladus and, for biochemistry based on a liquid other than water, Titan. If we find evidence for a second genesis of life, we will certainly learn from the comparative study of the biochemistry, organismal biology and ecology of the alien life. The discovery of alien life, if alive or revivable, will pose fundamentally new questions in environmental ethics. We should plan our exploration strategy such that we conduct biologically reversible exploration. In the long term we would do well, ethically and scientifically, to strive to support any alien life discovered as part of an overall commitment to enhancing the richness and diversity of life in the Universe.     

An Instance-Based Method for Remaining Useful Life Estimation for Aircraft Engines

Under customer service agreements (CSA), engine operational data are collected and stored for monitoring and analysis. Other data sources provide damage assessments that are either provided post-maintenance or analytically assessed. This paper takes advantage of these data and investigates local fuzzy models to determine the remaining useful life (RUL) of an engine or engine component. Local fuzzy models are related to both kernel regressions and locally weighted learning. The particular local models described in this paper are not based on individual models that consider the track history of a specific engine nor are they based on a global average model that would consider the collective track history of all the engines. Instead, for a given engine or component, this local fuzzy model defines a cluster of peers in which each of these peers is a similar instance to this given engine with comparable operational characteristics; the RUL prediction for this given engine is obtained by a fuzzy aggregation of its peers’ RUL. We combine the fuzzy instance-based approach with an evolutionary framework for model tuning and maintenance. This evolutionary tuning process is repeated periodically to automatically update and improve the fuzzy models such that they can be updated to date with the latest collection of data. This fuzzy instance-based approach is applied to predicting the RUL of a commercial engine validated with post-maintenance assessment. Reprinted with permission from Integration of Machinery Failure Prevention Technologies into Systems Health Management, Proceedings of the 61st Meeting of the Society for Machinery Failure Prevention Technology, Society for Machinery Failure Prevention Technology, 2007, on CD-ROM. This work was done while the author was with GE Global Research.     

Factory design and process optimisation with flexibility measurements in industrial production

Manufacturing companies operate in increasingly turbulent and discontinuous environments. Uncertain projections regarding future sales, large volume fluctuations, the steady advance of new technologies, and ever-shorter product life cycles, combined at the same time with increasing product variety, demand the constant adjustment of production structures to meet the current market requirements. Uncertainty in the production area requires a continuous transformation of production structures to meet the current requirements on the market. This poses a challenge for manufacturers in the various branches of industry and creates an ever-increasing need for flexibility to find an economic balance, in times of tight budgets, between the prevailing market uncertainties and the correct arrangement of the production systems. In the following document, the applied experience is presented, extracted from the flexibility assessment tool economical flexibility measurement (ecoFLEX). It will be shown along with the practical experience gained in its application at a car-engines plant.     

Time-varying extreme pattern with dynamic models

This paper is concerned with the analysis of time series data with time-varying extreme pattern. This is achieved via a model formulation that considers separately the central part and the tail of the distributions, using a two-component mixture model. Extremes beyond a threshold are assumed to follow a generalized Pareto distribution (GPD). Temporal dependence is induced by allowing the GPD parameters to vary with time. Temporal variation and dependence is introduced at a latent level via the novel use of dynamic linear models (DLM). Novelty lies in the time variation of the shape and scale parameter of the resulting distribution. These changes in limiting regimes as time changes reflect better the data behavior, with important gains in estimation and interpretation. The central part follows a nonparametric mixture approach. The uncertainty about the threshold is explicitly considered. Posterior inference is performed through Markov Chain Monte Carlo (MCMC) methods. A variety of scenarios can be entertained and include the possibility of alternation of presence and absence of a finite upper limit of the distribution for different time periods. Simulations are carried out in order to analyze the performance of our proposed model. We also apply the proposed model to financial time series: returns of Petrobrás stocks and SP500 index. Results show advantage of our proposal over currently entertained models such as stochastic volatility, with improved estimation of high quantiles and extremes.     

Semi-parametric estimation and forecasting for exogenous log-GARCH models

Advanced computing and processing techniques have yielded abundant information for financial time series forecasting. It is, therefore, natural to ask for possible extensions of time series models to accommodate the wealth of information. In this article, we develop a new model for financial volatility estimation and forecasting by incorporating exogenous covariates in a semi-parametric log-GARCH model. With additional information, we gain an increased prediction power. We propose a quasi-maximum likelihood procedure via spline smoothing technique. Consistent estimators and asymptotic normality are obtained under mild regularity conditions. Simulation experiments provide strong evidence that corroborates the asymptotic theories. Additionally, an application to SPY index data demonstrates strong competitive advantage of our model comparing with GARCH(1,1) and log-GARCH(1,1) models.     

Using Big Data to Discover Chaos in China’s Futures Market During COVID-19

COVID-19 was first reported in China and quickly spread throughout the world. Weak investor confidence in government efforts to control the pandemic seriously affected global financial markets. This study investigated chaos in China’s futures market during COVID-19, focusing on the degree of chaos at different periods during the pandemic. We constructed a phase diagram to observe the attractor trajectory of index futures (IFs). During the COVID-19 outbreak, overall chaos in China’s futures market was increasing, and there was a clear correlation between market volatility and the macroenvironment (mainly government regulation). The Hurst index, calculated by rescaled range (R/S) analysis, was 0.46. The price and return of IFs showed long-term correlation and fractal characteristics; the relevant dimensions of the futures market were 2.17. Overall, under the influence of an emergency (COVID-19), chaos in China’s financial market intensified, creating a need for timely government intervention and macrocontrol of the market. This study’s findings can help improve the government’s understanding of the phenomenon of financial chaos caused by emergencies. This study also provides theoretical guidance for controlling financial chaos and maintaining healthy economic development when faced with similar events in the future.     

Exploring Collective Dynamics in Communication Networks

A communication network, such as the Internet, comprises a complex system where cooperative phenomena may emerge from interactions among various traffic flows generated and forwarded by individual nodes. To identify and understand such phenomena, we model a network as a two-dimensional cellular automaton. We suspect such models can promote better understanding of the spatial-temporal evolution of network congestion, and other emergent phenomena in communication networks. To search the behavior space of the model, we study dynamic patterns arising from interactions among traffic flows routed across shared network nodes, as we employ various configurations of parameters and two different congestion-control algorithms. In this paper, we characterize correlation in congestion behavior within the model at different system sizes and time granularities. As expected, we find that long-range dependence (LRD) appears at some time granularities, and that for a given network size LRD decays as time granularity increases. As network size increases, we find that long-range dependence exists at larger time scales. To distinguish effects due to network size from effects due to collective phenomena, we compare congestion behavior within networks of selected sizes to congestion behavior within comparably sized sub-areas in a larger network. We find stronger long-range dependence for sub-areas within the larger network. This suggests the importance of modeling networks of sufficiently large size when studying the effects of collective dynamics.     

Cutting down the travel distance of put systems at Kunming International Flower Auction Market

At Kunming International Flower Auction Market (KIFA), about 2.5 million cut flowers traded in 10,000 transactions need to be distributed daily to buyers in its distribution area. Small lots and many buyers per trolley are two distinctive features at KIFA and the identities of the buyers and their demands are not known in advance. The growing transaction volume has recently increased the distribution workforce and the buyers’ waiting time. In this paper, we introduce a modified class-based location policy using KIFA’s historical data to improve its current put system performance. We use the closest-open location method in each class area, which improves the put system performance at KIFA. We examine the effects of the distribution area shape and the number of blocks in each class area on performance measures, and find that KIFA’s put system performance can be further improved.     

Technological frames of car engines

In this paper we analyze how car users frame car engines. Car engines have been build on internal combustion (IC) technologies for more than hundred years now; however, in the last decade hybrid-electric engines have been successfully introduced. The coexistence of conventional and unconventional technologies raises the interesting question to what extent these different engine technologies are framed differently, and how they change over time. Studies of technological frames and mental models suggest that frames of established technologies are more obdurate.In this paper we analyze technological frames of a few types of car engines and how frames have shifted during market evolution between 1990 and 2005. We find that engines are framed rather differently. Frames of conventional diesel engines emphasize engine capacity (measured in kW), engine volume (measured in liters) and torque (measured in Nm), whereas for hybrids fuel efficiency is by far the most prominent attribute. Further we find that the frame of the conventional engine (diesel) is more stable than that of an unconventional engine (hybrid and full-electric), which confirms the obduracy hypothesis that is raised in earlier studies of technological frames.     

Determination of batch size at a bottleneck machine in manufacturing systems

In manufacturing systems, there often exists a bottleneck machine whose capacity is equal to or less than the market demand. Any idle or waste time at the bottleneck machine directly impacts the output of the entire plant because it results in a loss of throughput. In order to maximize the capacity utilization by less setup losses at the bottleneck machine, the parts are often produced in batches. Traditionally, most batch sizing decisions are made based on the economic order quantity model where setup and inventory holding costs are considered. This paper presents an alternative method to determine batch size at a bottleneck machine. We present a new objective function and cost factors for batch sizing and investigate queuing and throughput models. A linear search algorithm is introduced to find the optimal throughput rate and batch size at the same time. Numerical examples are examined to see how the batching algorithm works.