A decision tree approach for selecting between demand based,reorder, and JIT/kanban methods for material procurement

In designing mechanisms for the procurement of raw materials, a manager is often faced with the task of selecting from a variety of possible methods. Since the performance of these methods is a function of the environment in which they are implemented, it is the responsibility of the manager to select the method best suited for a particular application. In this paper we present a methodology for assisting managers in the selection process. Specifically, three classes of procurement methods are considered, these are: demand based, reorder, and JIT methods. The selection methodology is structured as a decision tree, which considers the annual usage value, lead time ratio, holding cost, vendor location, vendor delivery performance, and material supply value. Each factor is evaluated on a three level scale, and on the basis of these evaluations the tree makes a selection. The application of the methodology is illustrated via some examples.     

A constitutional theory of the family

The paper re-examines the idea that a family can be viewed as a community governed by a self-enforcing constitution, and extends existing results in two directions. First, it identifies the circumstances in which a constitution is renegotiation-proof. Second, it introduces parental altruism. The behavioural and policy implications are illustrated by showing the effects of public pensions and credit rationing. These implications are not much affected by whether altruism is assumed or not, but contrast sharply with those of more conventional models.

解读赛珍珠的“自我反思”

赛珍珠去世前不久,美国传记作家欧文.布洛克对他进行了一次较长时间的访问,为《美国妇女丛书》写下《赛珍珠传略》。其中她把自己的童年时代、婚姻情况和哲学思想等方面,做了详尽的答问和总结,其中有些内容没有公开发表过。本文就此内容参阅其他文献进行分析、探讨与解释,供读者和赛珍珠研究者参考。     

Outliers in incomplete multiresponse experiments

Identification of outlier vectors in block designs for incomplete multiresponse experiments has been considered. Design is composed of two sets of experimental units. Different numbers of response variables are observed from these two sets. Cook-statistic has been developed for identification of outliers. The developed statistic has been illustrated with a real-life data set. It has been shown that presence of outliers can distort the overall conclusion from an experiment.     

Relationships of optimality for individual factors of a design

The optimality of two-factor experimental designs is studied in the dual senses of estimating contrasts in the parameters for each of the factors. The outline of comparison employed allows one to judge the performance of different designs for estimating contrasts of one set of parameters directly with the performance of the complementary set without going through a common intermediary step of considering all the parameters. The results hold for a wide class of optimality criteria (not merely D-, A- and E-optimality), which must satisfy a functional equation obtained in connection with our method. Also we investigate the optimality of row–column designs which satisfy an ‘adjusted orthogonality’ condition. Our point of departure is the paper by Shah, Raghavarao and Khatri (1976) and that of Mitchell and John (1977).     

Generalized Youden designs: construction and tables

Generalized Youden Designs are generalizations of the class of two-way balanced block designs which include Latin squares and Youden squares. They are used for the same purposes and in the same way that these classical designs are used, and satisfy most of the common criteria of design optimality.We explicitly display or give detailed instructions for constructing all these designs within a practical range: when υ, the number of treatments, is ?25; and b₁ and b₂, the dimensions of the design array, are each ?50.     

南水北调工程下穿高速公路暗涵施工控制技术

南水北调是国家重点工程,下穿既有结构物的大型暗涵是南水北调工程中一种比较典型的结构形式。下穿段浅埋暗挖施工过程中洞内安全和地面环境问题突出;对于大型钢筋混凝土结构而言,施工中防温裂技术难度大,影响因素错综复杂,一旦控制不当,容易造成结构裂缝超限,严重影响暗涵结构的耐久性和使用功能。论文以南水北调中线古运河枢纽工程下穿石太高速公路段为背景,对既有高速公路路面沉降和箱涵结构混凝土限裂温控标准进行了分析研究。研究认为,施工过程中既有路面沉降应控制在 100 mm以内、结构混凝土内部温降应控制在54.4 ℃以下,     

Robust block bootstrap panel predictability tests*

This article develops two block bootstrap-based panel predictability test procedures that are valid under very general conditions. Some of the allowable features include cross-sectional dependence, heterogeneous predictive slopes, persistent predictors, and complex error dynamics, including cross-unit endogeneity. While the first test procedure tests if there is any predictability at all, the second procedure determines the units for which predictability holds in case of a rejection by the first. A weak unit root framework is adopted to allow persistent predictors, and a novel theory is developed to establish asymptotic validity of the proposed bootstrap. Simulations are used to evaluate the performance of our tests in small samples, and their implementation is illustrated through an empirical application to stock returns.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

Bootstrap confidence regions computed from autoregressions of arbitrary order

Given a linear time series, e.g. an autoregression of infinite order, we may construct a finite order approximation and use that as the basis for confidence regions. The sieve or autoregressive bootstrap, as this method is often called, is generally seen as a competitor with the better-understood block bootstrap approach. However, in the present paper we argue that, for linear time series, the sieve bootstrap has significantly better performance than blocking methods and offers a wider range of opportunities. In particular, since it does not corrupt second-order properties then it may be used in a double-bootstrap form, with the second bootstrap application being employed to calibrate a basic percentile method confidence interval. This approach confers second-order accuracy without the need to estimate variance. That offers substantial benefits, since variances of statistics based on time series can be difficult to estimate reliably, and—partly because of the relatively small amount of information contained in a dependent process—are notorious for causing problems when used to Studentize. Other advantages of the sieve bootstrap include considerably greater robustness against variations in the choice of the tuning parameter, here equal to the autoregressive order, and the fact that, in contradistinction to the case of the block bootstrap, the percentile t version of the sieve bootstrap may be based on the 'raw' estimator of standard error. In the process of establishing these properties we show that the sieve bootstrap is second order correct.