Supply chain management: a supplier perspective

A new way of researching into supply chain management is introduced by adopting a supplier perspective. Details are given of mixed integer linear planning and simulation models. The planning model takes into consideration firm and forecast orders (customer's forecast purchasing orders) and the behaviour of the supplier's suppliers and suppliers' subcontractors. The simulation model takes into account the dynamic behaviour of the supply chain and includes the planning production behaviour of a supplying company based on the planning model. The supplier manager can use the simulation model to determine what kind of parameters most affect company performances and then propose new management rules. Quantitative results that prove the benefits of integrating forecast orders for an aeronautic supplier have been provided.     

An approximation to the convolution of gamma distributions

In general, the exact distribution of a convolution of independent gamma random variables is quite complicated and does not admit a closed form. Of all the distributions proposed, the gamma-series representation of Moschopoulos (1985 Moschopoulos, P. G. (1985). The distribution of the sum of independent gamma random variables. Annals of the Institute of Statistical Mathematics 37Part A:541–544. [Google Scholar]) is relatively simple to implement but for particular combinations of scale and/or shape parameters the computation of the weights of the series can result in complications with too much time consuming to allow a large-scale application. Recently, a compact random parameter representation of the convolution has been proposed by Vellaisamy and Upadhye (2009 Vellaisamy, P., Upadhye, N. S. (2009). On the sums of compound negative binomial and gamma random variables. Journal of Applied Probability 46:272–283.[Crossref], [Web of Science ®] , [Google Scholar]) and it allows to give an exact interpretation to the weights of the series. They describe an infinite discrete probability distribution. This result suggested to approximate Moschopoulos’s expression looking for an approximating theoretical discrete distribution for the weights of the series. More precisely, we propose a general negative binomial distribution. The result is an “excellent” approximation, fast and simple to implement for any parameter combination.     

Statistical analysis of error for fourth-order ordinary differential equation solvers

We develop an autoregressive integrated moving average (ARIMA) model to study the statistical behavior of the numerical error generated from three fourth-order ordinary differential equation solvers: Milne's method, Adams–Bashforth method and a new method that randomly switches between the Milne and Adams–Bashforth methods. With the actual error data based on three differential equations, we desire to identify an ARIMA model for each data series. Results show that some of the data series can be described by ARIMA models but others cannot. Based on the mathematical form of the numerical error, other statistical models should be investigated in the future. Finally, we assess the multivariate normality of the sample mean error generated by the switching method.     

A new random permutation test in ANOVA models

This paper developed an exact method of random permutations when testing both interaction and main effects in the two-way ANOVA model. The method of this paper can be regarded as a much improved model when compared with those of the previous studies such as Still and White (1981) and ter Braak (1992). We further conducted a simulation experiment in order to check the statistical performance of the proposed method. The proposed method works relatively well for small sample sizes compare with the existing methods. This work was supported by Korea Science and Engineering Foundation Grant (R14-2003-002-0100)     

动静叶相互干涉的三维非定常流场数值模拟

将双时间步法应用于叶栅级的全三维黏性非定常数值求解，讨论了分区算法中内边界条件的给定问题，本算法可保证分区边界上具有与内点相同的精度，具有很好的数值稳定性，且通量基本守恒，将该方法应用于具有弯曲导叶的涡轮级的计算可得到典型的非定常流谱，揭示了动静叶之间的相互干涉对叶片气动性能的影响，数值计算结果表明本文给出的算法是合理有效的。     

带有马尔可夫调制和跳的随机微分延迟方程Euler方法的强相合性

本文主要研究了下列形式的随机微分延迟方程：dX（t） =f（X（t） ,X（t -τ（t） ） ,r（t） ）dt ＋g（X（t） ,X（t -τ（t） ） ,r（t） ）dW（t） ＋h（X（t） ,X（t -τ（t） ）, r（t） ）dN（t） 0≤t≤T.考虑了时间延迟．r（t）为变量，Euler方法数值解；给出并且证明了Euler方法的强相合性定理，即Euler方法数值解均方意义下局部收敛于精确解．     

Reverse Regression and Employment Discrimination

The importance of interval forecasts is reviewed. Several general approaches to calculating such forecasts are described and compared. They include the use of theoretical formulas based on a fitted probability model (with or without a correction for parameter uncertainty), various “approximate” formulas (which should be avoided), and empirically based, simulation, and resampling procedures. The latter are useful when theoretical formulas are not available or there are doubts about some model assumptions. The distinction between a forecasting method and a forecasting model is expounded. For large groups of series, a forecasting method may be chosen in a fairly ad hoc way. With appropriate checks, it may be possible to base interval forecasts on the model for which the method is optimal. It is certainly unsound to use a model for which the method is not optimal, but, strangely, this is sometimes done. Some general comments are made as to why prediction intervals tend to be too narrow in practice to encompass the required proportion of future observations. An example demonstrates the overriding importance of careful model specification. In particular, when data are “nearly nonstationary,” the difference between fitting a stationary and a nonstationary model is critical.     

A low-cost approach to computer-integrated manufacturing in a small chemical unit

Many firms are considering CIM as a means of reducing costs and increasing quality. Sometimes, however, a major problem in this kind of project seems to be the high investments involved. In this paper a methodology for developing a low-cost CIM application is presented. The proposed methodology was successfully experimented in a small chemical unit of one of the leading producers of nylon in the world. The paper reports on the design and implementation of the computer-integrated manufacturing and the associated costs and benefits.     

Estimation of Deprivation Level Functions using a Numerical Rating Scale

Evaluating and quantifying human suffering in humanitarian operations offers an innovative and potentially powerful way to assess the performance of humanitarian logistics (HL) and help build optimization models. Previous studies have suggested deprivation cost as a metric and have estimated deprivation cost functions for water using willingness‐to‐pay. Our study proposes deprivation levels, defined as the degree of human suffering caused by lack of access to a good or service, and estimates deprivation level functions using a numerical rating scale. Analyzing data collected from respondents with and without disaster experience, we find that individuals in the latter category estimate deprivation differently from the beneficiaries of disaster relief. Our study demonstrates that deprivation levels can be expressed as logistic growth functions with a typical S‐shape, and that these can be integrated into HL optimization models to better account for human suffering.     

Application of numerical interval analysis to obtain self-validating results for multivariate probabilities in a massively parallel environment

Conventional computations use real numbers as input and produce real numbers as results without any indication of the accuracy. Interval analysis, instead, uses interval elements throughout the computation and produces intervals as output with the guarantee that the true results are contained in them. One major use for interval analysis in statistics is to get results of high-dimensional multivariate probabilities. With the efforts to decrease the length of the intervals that contain the theoretically true answers, we can obtain results to any arbitrary accuracy, which is demonstrated by multivariate normal and multivariate t integrations. This is an advantage over the approximation methods that are currently in use. Since interval analysis is more computationally intensive than traditional computing, a MasPar parallel computer is used in this research to improve performance.