The governing dynamics of supply chains: The impact of altruistic behaviour

This paper analyses an infinite horizon two-echelon supply chain inventory problem and shows that a sequence of the optimum ordering policies does not yield globally optimal solutions for the overall supply chain. First-order autoregressive demand pattern is assumed and each participant adopts the order-up-to (OUT) policy with a minimum mean square error forecasting scheme to generate replenishment orders. To control the dynamics of the supply chain, a proportional controller is incorporated into the OUT policy, which we call a generalised OUT policy. A two-echelon supply chain with this generalised OUT policy achieves over 10% inventory related cost reduction. To enjoy this cost saving, the attitude of first echelon player to cost increases is an essential factor. This attitude also reduces the bullwhip effect. An important insight revealed herein is that a significant amount of benefit comes from the player doing what is the best for the overall supply chain, rather than what is the best for local cost minimisation.     

武器装备可修件三级库存模型研究

关于武器装备可修件优化管理问题,针对部队中由于保障配置不合理而造成各级仓库普遍存在的"备件匮乏和大量积压"问题,通过借鉴国内外可修件库存理论研究成果,依据武器装备可修件的实际需求规律,结合修理能力约束因素,以备件订购费用为约束条件,以备件期望延迟交货量最小或装备可用度最大为目标,构建了一个可修件三级库存模型,并结合实例进行仿真,验证了模型的可行性,并可以得到武器装备可修件多级情况下的库存优化策略,实现备件保障的军事效益和经济效益,同时为备件优化管理进行库存决策提供了技术手段。     

卡尔曼实时跟踪模型在股票价格预测中的应用

基于卡尔曼滤波的动态、实时跟踪性以及股票市场的易波动性,该文提出了将股票视为一个机动物体,其价格视为该物体的位移,其价格的变化视为该物体的速度,依据非线性物理动力学模型来描述股票价格的波动,并且利用卡尔曼滤波理论建立了一种动态的股票价格预测模型,最后给出了相应的算法.通过实例仿真,并对结果进行分析表明,本文提出的算法具有可靠、计算简便、快速等特点,模型预测精度较高,并可实现实时跟踪预测,具有一定的理论价值和实用价值.     

人体下肢肌力分布的仿真模型及参数影响研究

肌力的分布对于更好地理解单个肌肉作用和负载情况有着重要的指导意义。该文建立了三自由度9块肌肉的人体下肢肌力分布求解模型，并利用该模型仿真计算了跑步运动缓冲阶段和蹬伸阶段的肌力分布规律，比较了不同目标函数和肌肉生理横断面积的变化对肌力分布的影响作用。数值计算的结果显示以最小应力立方和为优化目标可获得较好的优化性能，而且肌肉生理横断面积的增加使得相应肌肉的肌力有所增加同时也影响其它肌肉的肌力变化。该方法可用于分析跑步运动过程中下肢肌肉相互协调的功能特性，并且为在体育训练中有针对性的进行肌肉力量训练以及改进技术动作提供了一定的参考数据。     

Supporting Semantic Diversity in the Relational Data Model: The Case of Multi-Face Attributes

In recent years various proposals have been offered for increasing the richness of the relational data model by addressing specific user requirements, particularly with regard to structural and behavioral expressiveness. Although there have been some criticisms of the semantic limitations of the model, few proposals have emerged to address them. In this paper we propose an extension of the model to address some of the semantic limitations around the use of multi-face attributes in everyday activity. We present a formal discussion of multi-face attributes and suggest how they can be accommodated in the relational model and relational database software systems. The resulting model offers a higher-level expressiveness and will provide the user with increased flexibility in the input, output and storage of data, and the specification of queries. Finally we outline some of the implications for database design methods that emerge for the extension.     

基于遗传算法的灌区通用水资源整体优化配置模型

解决水资源的优化问题大多采用开源节流、加强管理等措施,而这只能是解决问题的外因。水资源的合理配置问题,最好由用水者的利益激励机制来解决,这样才能从根本上解决水资源的短缺和低效率应用问题。基于遗传算法,结合利益激励机制,对冯家山水资源优化配置方案进行探讨。为水资源分配和管理提供参考。     

Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model

The behavioral origins of the stylized facts of financial returns have been addressed in a growing body of agent-based models of financial markets. While the traditional efficient market viewpoint explains all statistical properties of returns by similar features of the news arrival process, the more recent behavioral finance models explain them as imprints of universal patterns of interaction in these markets. In this paper we contribute to this literature by introducing a very simple agent-based model in which the ubiquitous stylized facts (fat tails, volatility clustering) are emergent properties of the interaction among traders. The simplicity of the model allows us to estimate the underlying parameters, since it is possible to derive a closed form solution for the distribution of returns. We show that the tail shape characterizing the fatness of the unconditional distribution of returns can be directly derived from some structural variables that govern the traders’ interactions, namely the herding propensity and the autonomous switching tendency.JEL classifications: G12; C61Earlier versions of this paper have been presented at the 11th Symposium of the Society of Nonlinear Dynamics and Econometrics, Florence, March 2003, the 8th Spring Meeting of Young Economists, Leuven, April 2003, the 8th Workshop on Economics with Heterogeneous Interacting Agents, Kiel, May 2003, the 27th congress of Associazione per la Matematica Applicata alle Scienze Economiche e Sociali, Cagliari, September 2003; research seminars at the Department of Econometrics, University of Geneva, March 2003, and at the Department of Physics, University of Cagliari, May 2003, and have gained considerably from comments by many participants in these events.     

Integration of Path-Dependency in a Simple Learning Model: The Case of Marine Resources

Overexploitation of renewable resources, and more particularly fisheries, is often driven by the lack of information about the state and dynamics of the resource. A solution to this problem stemming from the resource users is proposed in this paper. We use an agent-based model composed of a bio-economic model of Gordon–Schaefer where agents make choices following a very simple learning model. We modify the Roth–Erev learning model so that agents explain their profit not only by current action but also by past action. This modification radically changes the dynamics of the resource use, which turns out to be sustainable.     

Mining User Knowledge for Investigating the Facebook Business Model: The Case of Taiwan Users

Social network sites (SNS), as web-based services, allow users to make open or semi-open profiles within the systems they are part of, to see lists of other people in the group and to see the relations of people within different groups. Facebook is essentially an online social network site in which individuals can share photographs, personal information, and join groups of friends. This study investigates the experiences on Facebook of various users in Taiwan. Their degrees of confidence were often demonstrated by word-of-mouth disseminations about the social network site. Further, this research looks at how the reputations of Facebook proprietors and their affiliates were disseminated through relationship marketing for formulated social network marketing in its business model concerns. Therefore, this study uses the a priori algorithm as an association rules approach, and cluster analysis for data mining. We divide Facebook users into two groups of contributors and lurkers by their profiles and then find each group’s social network community information utilization and online purchase behaviors for investigating the Facebook business models.     

Detecting stock-price manipulation in an emerging market: The case of Turkey

This paper aims to develop methods that are capable of detecting manipulation in the Istanbul Stock Exchange. We take the difference between manipulated stock’s and index’s average daily return, average daily change in trading volume and average daily volatility and used these statistics as explanatory variables. The data in post-manipulation and pre-manipulation periods are used as non-manipulated instances while the data in the manipulation period are used as manipulated instances. Test performance of classification accuracy, sensitivity and specificity statistics for Artificial Neural Networks (ANN) and Support Vector Machine (SVM) are compared with the results of discriminant analysis and logistics regression (logit). We found that the data mining techniques (ANN and SVM) are better suited to detect stock-price manipulation than multivariate statistical techniques (discriminant analysis, logistics regression) as the performances of the data mining techniques in terms of total classification accuracy and sensitivity statistics are better than those of multivariate techniques. We also found that unit change in difference between average daily return of manipulated stock and the index has the largest effect while unit change in difference between average daily change in trading volume of manipulated stock and index has the least effect on multivariate classifiers’ decision functions.     

Supply planning under uncertainties in MRP environments: A state of the art

Inventory control in a supply chain is crucial for companies desiring to satisfy their customers demands as well as controlling costs. This paper examines specifically supply planning under uncertainties in MRP environments. Models from literature that deal with random demand or lead time uncertainties are described and commented. Promising research areas emerge from this survey. It appears that lead time uncertainty has been ignored in the past, in spite of their significant importance. In particular, an interesting topic concerns assembly systems with uncertain lead times, for which the main difficulty comes from the inter-dependence of components inventories. Another promising issue, which is also presented, relates to supply planning under simultaneously demand and lead time uncertainties, which is certainly of great interest for both the academic and industrial communities.     

案例教学法在C语言教学中的研究与实践

案例教学法作为一种新的教学方法,被广泛用于MBA、医学和法学等教学领域。本文结合C语言的特点,阐述了案例教学模式和方法在C语言中的应用,并把它运用在教学实践中。     

间歇过程生产调度建模的研究

生产调度是为实现某一目的而对共同使用的资源进行时间上的分配.调度中存在大量的模糊因素,将模糊的思想运用到调度领域可以帮助决策者进行有效决策.本文提出一种基于模糊规划的间歇过程生产调度建模方法及其模糊优化的新算法.应用模糊集合论的方法,在已有模糊规划模型的基础上,针对间歇生产过程,分析调度中存在的模糊信息以及出现的形式和方式,将确定型生产调度模型的约束条件和目标方程中的参数模糊化,采用非精确的量化形式,以隶属函数来表示,建立基于模糊参数的生产调度模糊线性规划模型MIFCLP.通过对一个调度问题实例进行了仿真,仿真结果证明,采用模糊模型更容易得到可行解,采用模糊线性规划解决间歇生产调度问题是一种有效方法.     

变频器在电厂风机中的应用研究

利用变频器技术对电厂的风机进行改造是节能降耗并改善系统性能的重要方式之一 ,特别是对机组变负荷的运行 ,该文叙述并分析了高压变频器在电厂风机中的应用实例和应用中的特性。     

The machine breakdown paradox: How random shifts in the production rate may increase company profits

The causes and effects of machine breakdowns have frequently been investigated in the past. One popular stream of research studies technical errors in production and analyzes their impact on the inventory policy of the company. In this paper, we show that random shifts in the production rate of a machine, which may occur, for example, due to technical defects, may lead to a reduction in total cost and therewith to an increase in profit. This obvious paradox may lead to situations where it is economically rational for the company to sustain a technically inefficient situation, or even to take measures to intentionally induce a shift in the production rate, for example by damaging the machine on purpose. In this paper, we illustrate this paradox by referring to an existing inventory model, and trace it back to common assumptions made in the literature.     

海量数据预测模型设计及案例分析

预测是很多行业都需要的一项方法和技术,随着数据积累的越来越多,现在许多行业大多面临基于海量数据的预测问题,该文从基于海量数据挖掘的预测方法出发,给出了一个数据挖掘预测系统的模型,并针对一个行业案例介绍了预测的具体处理过程,最后对预测结果的评价和选取情况进行了分析。     

现场总线检测系统的信息融合方法及其应用

本文总结了现场总线检测系统的几种信息融合方法,给出了一个应用实例,这些方法的算法简单,实时性强,适合检测系统的信息融合。     

An Investment Decision Model with the Survival Probability Criterion and its Numerical Solutions: The Finite Horizon Case

This paper studies a finite horizon investment decision model. Suppose that an investor is endowed with initial wealth in the beginning. At every stage, he needs to consume a part of his wealth and allocate the rest between a risky and a riskless asset. The investor wishes to maximize the survival probability that his wealth can satisfy the consumption requirements during the horizon and reach a disaster level at the end. Since the allocation decision depends on not only his wealth but also the disaster level, we introduce a Markov decision process based on decision space to describe the investment behavior of the investor and prove the existence of a deterministic Markov optimal policy. An algorithm to compute the optimal policy and the maximal probability of survival is given and four numerical examples are discussed.     

Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules

By nature, sampling is an appealing technique for data mining, because approximate solutions in most cases may already be of great satisfaction to the need of the users. We attempt to use sampling techniques to address the problem of maintaining discovered association rules. Some studies have been done on the problem of maintaining the discovered association rules when updates are made to the database. All proposed methods must examine not only the changed part but also the unchanged part in the original database, which is very large, and hence take much time. Worse yet, if the updates on the rules are performed frequently on the database but the underlying rule set has not changed much, then the effort could be mostly wasted. In this paper, we devise an algorithm which employs sampling techniques to estimate the difference between the association rules in a database before and after the database is updated. The estimated difference can be used to determine whether we should update the mined association rules or not. If the estimated difference is small, then the rules in the original database is still a good approximation to those in the updated database. Hence, we do not have to spend the resources to update the rules. We can accumulate more updates before actually updating the rules, thereby avoiding the overheads of updating the rules too frequently. Experimental results show that our algorithm is very efficient and highly accurate.     

The BINAC: A Case Study in the History of Technology

The BINAC, short for Binary Automatic Computer, was developed by John Presper Eckert, Jr. and John William Mauchly during the years 1947-1949 under a contract with the Northrop Aircraft Corporation. It became the first operational stored program computer completed in the United States. This paper provides an historical analysis of the BINAC and the issues relating to its development. It also considers factors relating to the Eckert-Mauchly Computer Corporation and its ultimate acquisition by Remington Rand.