Multiobjective optimisation of production,distribution and capacity planning of global supply chains in the process industry

The performance of a supply chain should usually be measured by multiple criteria. We address production, distribution and capacity planning of global supply chains considering cost, responsiveness and customer service level simultaneously. A multiobjective mixed-integer linear programming (MILP) approach is developed with total cost, total flow time and total lost sales as key objectives. Also, two strategies to expand the formulation plants’ capacities are considered in the model. The ε-constraint method and lexicographic minimax method are used as solution approaches to tackle the multiobjective problem. Finally, a numerical example is investigated to demonstrate the applicability of the proposed model and solution approaches.     

A necessary and sufficient condition for an estimator to be optimal

A necessary condition for an estimator δ₀ to be optimal in a class Δ of estimators is derived. Thic condition becomes also sufficient when the loss function is convex.     

Eco-efficient supply chain networks: development of a design framework and application to a real case study

This paper presents a supply chain network design framework that is based on multi-objective mathematical programming and that can identify ‘eco-efficient’ configuration alternatives that are both efficient and ecologically sound. This work is original in that it encompasses the environmental impact of both transportation and warehousing activities. We apply the proposed framework to a real-life case study (i.e. Lindt & Sprüngli) for the distribution of chocolate products. The results show that cost-driven network optimisation may lead to beneficial effects for the environment and that a minor increase in distribution costs can be offset by a major improvement in environmental performance. This paper contributes to the body of knowledge on eco-efficient supply chain design and closes the missing link between model-based methods and empirical applied research. It also generates insights into the growing debate on the trade-off between the economic and environmental performance of supply chains, supporting organisations in the eco-efficient configuration of their supply chains.     

An Influence Function Approach to Describing the Skewness of a Distribution

Skewness, like kurtosis, is a qualitative property of a distribution. A comparison of several measures of skewness of univariate distributions is carried out. Hampel's influence function is used to clarify the differences and similarities among these measures. A general concept of skewness as a location- and scale-free deformation of the probability mass of a symmetric distribution emerges. Positive skewness can be thought of as resulting from movement of mass at the right of the median from the center to the right tail of the distribution together with movement of mass at the left of the median from the left tail to the center of the distribution.     

Convergence rates of generalization errors for margin-based classification

This paper develops a general approach to quantifying the size of generalization errors for margin-based classification. A trade-off between geometric margins and training errors is exhibited along with the complexity of a binary classification problem. Consequently, this results in dealing with learning theory in a broader framework, in particular, of handling both convex and non-convex margin classifiers, among which includes, support vector machines, kernel logistic regression, and ψ$\psi$-learning. Examples for both linear and nonlinear classifications are provided.     

一种新的凸二次规划的内点算法

提出了一个新的求解凸二次规划的内点算法 算法基于原始 -对偶仿射尺度算法的思想 ,每步迭代只须解一个线性方程组 ,通过适当选取步长 ,算法具有多项式计算复杂性     

On maximum likelihood estimation of the semi-parametric Cox model with time-varying covariates

Including time-varying covariates is a popular extension to the Cox model and a suitable approach for dealing with non-proportional hazards. However, partial likelihood (PL) estimation of this model has three shortcomings: (i) estimated regression coefficients can be less accurate in small samples with heavy censoring; (ii) the baseline hazard is not directly estimated and (iii) a covariance matrix for both the regression coefficients and the baseline hazard is not easily produced.We address these by developing a maximum likelihood (ML) approach to jointly estimate regression coefficients and baseline hazard using a constrained optimisation ensuring the latter''s non-negativity. We demonstrate asymptotic properties of these estimates and show via simulation their increased accuracy compared to PL estimates in small samples and show our method produces smoother baseline hazard estimates than the Breslow estimator.Finally, we apply our method to two examples, including an important real-world financial example to estimate time to default for retail home loans. We demonstrate using our ML estimate for the baseline hazard can give much clearer corroboratory evidence of the ‘humped hazard’, whereby the risk of loan default rises to a peak and then later falls.     

确定共轭向量的一种新方法—平行法

本文参照Powell直接法,提出了一种确定共轭向量的新方法。该方法能保证得到的共轭向量的线性独立性,而且所需要的一维搜索次数比Powell方法要少一半。在最优化运算中,该方法具有二次收敛性,一般说来,在运算速度上也比Powell方法要快一些。     

Testing hypotheses about the common mean of normal distributions

An overview of hypothesis testing for the common mean of independent normal distributions is given. The case of two populations is studied in detail. A number of different types of tests are studied. Among them are a test based on the maximum of the two available t-tests, Fisher's combined test, a test based on Graybill–Deal's estimator, an approximation to the likelihood ratio test, and some tests derived using some Bayesian considerations for improper priors along with intuitive considerations. Based on some theoretical findings and mostly based on a Monte Carlo study the conclusions are that for the most part the Bayes-intuitive type tests are superior and can be recommended. When the variances of the populations are close the approximate likelihood ratio test does best.