一种考虑整数约束的环境效率评价MOISBMSE模型

传统的基于数据包络分析的环境效率评价常常忽略投入、期望产出和非期望产出为离散型变量的可能性,从而无法精确处理现实中存在的具有整数特征的指标数据,一直以来影响着评价结果的精确性.本文以混合导向的整数DEA为基础,对超效率SBM模型进行拓展,构建了一种能够同时兼顾整数约束和非期望产出指标数据的超效率SBM模型,并在实例中采用搜索算法,对该模型进行求解并通过Bootstrap方法对结果进行修正.将每一个最优整数点与被评价决策单元之间的距离进行比较,可以找出最短距离所对应的最优整数点,即为其帕累托改进方向.该模型将为环境效率评价提供了适用范围更广泛的测度方法.     

并行工程产品开发过程定量化建模与计划制订

由于现有的一些并行工程定量化模型中修改设计微循环的细节特征描述得还不够充分 ,本文提出了一种新的基于产品 -工艺设计活动对网络的定量化模型来描述并行工程产品开发过程 ,并且给出了产品 -工艺设计活动对平均持续时间与产品或工艺设计活动资源占用率的计算方法 .在此基础上 ,将并行工程产品开发过程的计划制订问题建模成一个有资源约束的项目调度问题 .与其它此类问题不同的是 ,在本文中 ,分配给产品开发项目的各类资源的数量不是事先给定的 ,而是与最优产品开发计划一起获得的 ,所以这是一个资源分配与计划制订的集成优化问题 .本文提出了一种新的基于分枝定界的算法来解决此问题并引入一个启发式规则来提高算法的搜索效率     

考虑实时预倒箱的出口箱堆场多场桥调度优化

在出口集装箱堆场的实际作业过程中,倒箱是制约场桥作业效率的瓶颈之一。为提高出口箱堆场的作业效能,减少船舶装船作业时间,采用实时预倒来降低倒箱的影响,研究出口箱堆场多场桥调度优化问题。考虑待提箱作业次序固定、场桥间保持安全距离及不可跨越的现实约束,兼顾内集卡的等待上限,侧重场桥作业过程中的实时预倒箱,构建了以带惩罚因子的内集卡总等待时间最少为目标的混合整数线性规划模型。基于问题自身的特点设计了混合和声模拟退火算法,得出了各场桥的行走路径与实时预倒箱方案。在算例实验中,通过与不考虑实时预倒箱的方案、FCFS方案以及下界进行对比,验证了考虑实时预倒箱的场桥调度模型及算法的有效性,为集装箱码头出口箱堆场的场桥调度提供参考。     

集成化物流管理中库存路径问题研究综述

综合国内外多种参考文献,介绍了IRP的基本概念及其主要特征,从常见模型、求解策略及算法2个维度综述了当前国内外关于IRP的主要研究进展,最后指出了该研究领域在未来研究中应予以重视的几个研究方向,为进一步的研究奠定了基础.     

求解输出为有界随机数的排序择优问题

目的/意义排序择优问题是仿真优化领域的经典研究问题。该问题的目标是设计统计采样算法,通过在有限个统计分布中进行采样并观测随机采样结果从而找到真实均值最大的分布。在该问题的研究中,现有文献大多假设对不同分布进行采样时输出为正态分布随机数,进而基于正态分布随机数相关性质进行算法设计。但在现实中,该假设通常不成立,一旦假设不成立,现有算法的统计有效性将会大受影响。设计/方法将正态假设进行拓展,即假设对不同分布为有界域分布,进而开展算法设计。结论/发现设计出一类顺序淘汰式算法求解输出为有界域随机数的排序择优问题,数值实验验证,此算法效率远高于现有的SE、ME和lil′DCB算法。     

Higher-order Bayesian Approximations for Pseudo-posterior Distributions

The theory of higher-order asymptotics provides accurate approximations to posterior distributions for a scalar parameter of interest, and to the corresponding tail area, for practical use in Bayesian analysis. The aim of this article is to extend these approximations to pseudo-posterior distributions, e.g., posterior distributions based on a pseudo-likelihood function and a suitable prior, which are proved to be particularly useful when the full likelihood is analytically or computationally infeasible. In particular, from a theoretical point of view, we derive the Laplace approximation for a pseudo-posterior distribution, and for the corresponding tail area, for a scalar parameter of interest, also in the presence of nuisance parameters. From a computational point of view, starting from these higher-order approximations, we discuss the higher-order tail area (HOTA) algorithm useful to approximate marginal posterior distributions, and related quantities. Compared to standard Markov chain Monte Carlo methods, the main advantage of the HOTA algorithm is that it gives independent samples at a negligible computational cost. The relevant computations are illustrated by two examples.     

Computation of an efficient and robust estimator in a semiparametric mixture model

In this article, we propose an efficient and robust estimation for the semiparametric mixture model that is a mixture of unknown location-shifted symmetric distributions. Our estimation is derived by minimizing the profile Hellinger distance (MPHD) between the model and a nonparametric density estimate. We propose a simple and efficient algorithm to find the proposed MPHD estimation. Monte Carlo simulation study is conducted to examine the finite sample performance of the proposed procedure and to compare it with other existing methods. Based on our empirical studies, the newly proposed procedure works very competitively compared to the existing methods for normal component cases and much better for non-normal component cases. More importantly, the proposed procedure is robust when the data are contaminated with outlying observations. A real data application is also provided to illustrate the proposed estimation procedure.     

Fitting competing risks data to bivariate Pareto models

This paper revisits two bivariate Pareto models for fitting competing risks data. The first model is the Frank copula model, and the second one is a bivariate Pareto model introduced by Sankaran and Nair (1993 Sankaran, P. G., and N. U. Nair. 1993. A bivariate Pareto model and its applications to reliability. Naval Research Logistics 40 (7):1013–1020. doi:10.1002/1520-6750(199312)40:7%3c1013::AID-NAV3220400711%3e3.0.CO;2-7.[Crossref], [Web of Science ®] , [Google Scholar]). We discuss the identifiability issues of these models and develop the maximum likelihood estimation procedures including their computational algorithms and model-diagnostic procedures. Simulations are conducted to examine the performance of the maximum likelihood estimation. Real data are analyzed for illustration.     

Bias correction for parameter estimates of spatial point process models

When a spatial point process model is fitted to spatial point pattern data using standard software, the parameter estimates are typically biased. Contrary to folklore, the bias does not reflect weaknesses of the underlying mathematical methods, but is mainly due to the effects of discretization of the spatial domain. We investigate two approaches to correcting the bias: a Newton–Raphson-type correction and Richardson extrapolation. In simulation experiments, Richardson extrapolation performs best.     

Percentage points of the statistics for testing hypotheses on mean vectors of multivariate normal distributions with missing observations

A problem of testing of hypotheses on the mean vector of a multivariate normal distribution with unknown and positive definite covariance matrix is considered when a sample with a special, though not unusual, pattern of missing observations from that population is available. The approximate percentage points of the test statistic are obtained and their accuracy has been checked by comparing them with some exact percentage points which are calculated for complete samples and some special incomplete samples. The approximate percentage points are in good agreement with exact percentage points. The above work is extended to the problem of testing the hypothesis of equality of two mean vectors of two multivariate normal distributions with the same, unknown covariance matrix