Explicit filters and supply chain design

Due to the complexity of present day supply chains it is important to select the simplest supply chain scheduling decision support system (DSS) which will determine and place orders satisfactorily. We propose to use a generic design framework, termed the explicit filter methodology, to achieve this objective. In doing so we compare the explicit filter approach to the implicit filter approach utilised in previous OR research the latter focusing on minimising a cost function. Although the eventual results may well be similar with both approaches it is much clearer to the designer, both why and how, an ordering system will reduce the Bullwhip effect via the explicit filter approach. The “explicit filter” approach produces a range of DSS designs corresponding to best practice. These may be “mixed and matched” to generate a number of competitive delivery pipelines to suit the specific business scenario.     

物品配送路径优化的改进遗传算法

描述配送中心物品配送路径优化问题，并构造求解问题的改进遗传算法，在算法中引入模拟退火思想判断交叉操作后新个体的接受状态，同简单遗传算法相比，该算法能够增强全局收敛性，提高收敛速度。     

城市物流基地选址问题研究

本文将物流基地纳入城市交通网络中，以体现交通状况对物流基地选址的影响。在进行选址时，以运输费用和基地的投资、管理费用总和最小为规划者的主要目标，建立目标函数。选址的上层模型体现了运输费用和基地投资费用、管理费用最小的原则，下层模型体现了入选点的不同对交通流量分配的影响，下层模型是求解上层模型的基础。     

基于粒子群优化的模糊聚类分析

基于求解实优化问题时,粒子群优化算法优于遗传算法。在基于遗传算法的模糊C均值聚类算法基础上,给出了基于粒子群的模糊C均值聚类算法,试验结果表明:该算法克服了传统的模糊C均值聚类算法的缺陷,同时在收敛速度方面明显优于基于遗传算法的模糊C均值聚类算法。     

基于遗传算法的多目标集装箱多式联运运输优化模型

基于集装箱多式联运在进行门到门的运输过程中可以选择多种运输方式和路径的组合进行优化运输这个特点，本文将多式联运的运输优化问题转化成为一个最短路径问题，以成本和时间为优化目标建立了选择最优路径的模型，并选择遗传算法作为求解算法对实例问题进行了求解验证。     

物流中心仓库货位优化系统的研究

介绍了什么是货位优化,阐述货位优化技术是如何提高物流中心的操作效率及应用货位优化系统所带来的实际意义,比较了当今国外物流公司已推出的各种货位优化系统之间的区别,提出了开发该系统的基本思路。     

物流中心仓库货位优化系统的设计研究

以上海医药物流中心仓库为研究对象，为其仓库货位优化系统建立一个有效的多目标优化数学模型。并引入“类聚”的方法来解决同一产品族的货物临近放置的问题。最后，应用多目标遗传算法进行货位优化。     

基于启发式算法的成品油配送计划研究

通过对成品油配送计划特点的研究与分析，建立了成品油配送计划编制的数学模型；并设计了模型求解方案；通过对实验结果的分析，证明本文设计的算法快速、有效、稳定。     

越库作业调度问题及其启发式算法研究

主要研究两台机器环境下的以加权完工时间和为目标函数的越库调度问题。首先针对两机器越库调度问题进行研究与分析,给出该问题最优解的若干性质;其次,基于最优解的性质,提出求解该问题的启发式算法,并在此基础上对所给算法进行改进;最后,通过数值实验与动态规划算法比较,证明所给算法及其改进算法的有效性。     

ML– and semiparametric estimation in logistic models with incomplete covariate data

ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.