广义ATO下基于多种团购模型的供应链协调

针对由多个供应商、单个制造商和零售商所组成的广义按订单装配式供应链，在上游多个供应商不确定的零部件（或商品）供应以及下游客户不确定的需求的环境下，首先分析基本模型中团购前后的零售商和制造商的利润变化，然后提出了团购的标准模型和协调模型，并比较三种不同的团购模型对各成员以及全局供应链绩效的影响。通过数学推导证明和仿真数据算例分析，结果表明：零售商自发的团购基本模型总是对制造商有利，而对零售商的好处是有限的；由制造商主导的团购标准模型在满足一定条件时是优于团购基本模型的，但有使得零售商甚至制造商自身受到损失的可能；而集中决策下的团购协调模型能够提升全局供应链的期望利润，实现渠道的帕累托改善，但协调的效果会受到团购客户组成结构的限制。     

Incomplete categorical data analysis: a bayesian perspective

In this paper the Bayesian analysis of incomplete categorical data under informative general censoring proposed by Paulino and Pereira (1995) is revisited. That analysis is based on Dirichlet priors and can be applied to any missing data pattern. However, the known properties of the posterior distributions are scarce and therefore severe limitations to the posterior computations remain. Here is shown how a Monte Carlo simulation approach based on an alternative parameterisation can be used to overcome the former computational difficulties. The proposed simulation approach makes available the approximate estimation of general parametric functions and can be implemented in a very straightforward way.     

Economic and economic statistical designs for three-level control charts

In some situations, an appropriate quality measure uses three or more discrete levels to classify a product characteristic. For these situations, some control charts have been developed based on statistical criteria regardless of economic considerations. In this paper, we develop economic and economic statistical designs (ESD) for 3-level control charts. We apply the cost model proposed by Costa and Rahim.[Economic design of X charts with variable parameters: the Markov chain approach, J Appl Stat 28 (2001), 875–885] Furthermore, we assume that the length of time that the process remains in control is exponentially distributed which allows us to apply the Markov chain approach for developing the cost model. We apply a genetic algorithm to determine the optimal values of model parameters by minimizing the cost function. A numerical example is provided to illustrate the performance of the proposed models and to compare the cost of the pure economic and ESD for three-level control charts. A sensitivity analysis is also conducted in this numerical example.     

A note on spectral estimation for ARMA processes

The standard frequency domain approximation to the Gaussian likelihood of a sample from an ARMA process is considered. The Newton-Raphson and Gauss-Newton numerical maximisation algorithms are evaluated for this approximate likelihood and the relationships between these algorithms and those of Akaike and Hannan explored. In particular it is shown that Hannan's method has certain computational advantages compared to the other spectral estimation methods considered     

Finite Sample Performance of tests for Symmetry of the Errors in a Linear Model

The finite sample performance of a number of tests for symmetry of the distribution of the errors of a linear model is considered. The first family of tests is based on the discrepancy between two regression fits. The first fit is appropriate under symmetric errors while the second is appropriate for skewed as well as symmetric error distributions. The second family of procedures consists of tests for the univariate symmetry problem. Thus, in the linear model setting these tests are based on residuals. An extensive empirical study of the finite sample, null behavior of the tests is presented. The results of a power comparison among the tests is also discussed.     

Simulation techniques for generalized Gaussian densities

This contribution deals with the Monte Carlo simulation of generalized Gaussian random variables. Such a parametric family of distributions has been proposed in many applications in science to describe physical phenomena and in engineering, and it seems to be also useful in modelling economic and financial data. For values of the shape parameter α within a certain range, the distribution presents heavy tails. In particular, the cases α=1/3 and α=1/2 are considered. For such values of the shape parameter, different simulation methods are assessed.     

A Hellinger distance approach to MCMC diagnostics

Bayesian analysis often requires the researcher to employ Markov Chain Monte Carlo (MCMC) techniques to draw samples from a posterior distribution which in turn is used to make inferences. Currently, several approaches to determine convergence of the chain as well as sensitivities of the resulting inferences have been developed. This work develops a Hellinger distance approach to MCMC diagnostics. An approximation to the Hellinger distance between two distributions f and g based on sampling is introduced. This approximation is studied via simulation to determine the accuracy. A criterion for using this Hellinger distance for determining chain convergence is proposed as well as a criterion for sensitivity studies. These criteria are illustrated using a dataset concerning the Anguilla australis, an eel native to New Zealand.     

Rank invariant tests with left truncated and interval censored data

Peto and Peto (1972) have studied rank invariant tests to compare two survival curves for right censored data. We apply their tests, including the logrank test and the generalized Wilcoxon test, to left truncated and interval censored data. The significance levels of the tests are approximated by Monte Carlo permutation tests. Simulation studies are conducted to show their size and power under different distributional differences. In particular, the logrank test works well under the Cox proportional hazards alternatives, as for the usual right censored data. The methods are illustrated by the analysis of the Massachusetts Health Care Panel Study dataset.     

Latin hypercube sampling with multidimensional uniformity

Complex models can only be realized a limited number of times due to large computational requirements. Methods exist for generating input parameters for model realizations including Monte Carlo simulation (MCS) and Latin hypercube sampling (LHS). Recent algorithms such as maximinLHS seek to maximize the minimum distance between model inputs in the multivariate space. A novel extension of Latin hypercube sampling (LHSMDU) for multivariate models is developed here that increases the multidimensional uniformity of the input parameters through sequential realization elimination. Correlations are considered in the LHSMDU sampling matrix using a Cholesky decomposition of the correlation matrix. Computer code implementing the proposed algorithm supplements this article. A simulation study comparing MCS, LHS, maximinLHS and LHSMDU demonstrates that increased multidimensional uniformity can significantly improve realization efficiency and that LHSMDU is effective for large multivariate problems.     

Green supply chain management: the case of the construction sector in the United Arab Emirates (UAE)

Restraining the negative environmental impacts of the construction sector constitutes one of the major challenges of the twenty-first century. However, efforts to address it have been largely fragmented. With environmental consequences of a construction project typically dispersed across its life cycle, i.e. from design through to end-of-life, greening this sector requires a supply chain wide focus inclusive of all key stages and stakeholders; also, all relevant aspects such as the nature of green practices implemented and associated drivers, barriers and performance implications need to be considered. This forms the focus of the present study where a comprehensive, green supply chain management oriented understanding of the construction sector is developed through the context of the UAE construction sector, and incorporating inputs from all key stakeholders, i.e. Developers, Architects/Consultants, Contractors and (material) Suppliers. The study contributes to improving the efficiency and effectiveness of greening of the construction sector.