基于遗传算法的装备采购决策优化研究

在装备采购中，由于需求单位地域分布和担负的任务各不相同，对装备的品种、数量、时限要求也就不一样，如何使装备采购科学化、合理化，是一个涉及多变量、多目标的复杂系统问题。在综合考虑装备采购各项因素的基础上，构建多约束条件下的多目标模糊指派模型，提出了基于遗传算法的解决方案，最后通过案例进行仿真实验，验证该算法的可行性和有效性，解决了采用传统优化方法难以解决的装备采购优化决策问题。     

零售4.0时代先行发货问题的大数据分析

在零售4.0时代，渠道的多样化不仅丰富了数据源，还能迅速生成大量数据，需要通过分析大数据，为决策提取有意义的信息，通过分析先行发货的重要性，提出了一种基于遗传算法（GA）的优化模型，预测顾客何时购买，然后在顾客线下单前将产品运送到距顾客最近的配送中心，解决先行发货中存在的问题。研究认为，需要先部署云计算来存储所有渠道生成的大数据，再应用基于集群的关联规则挖掘研究顾客的购买行为，根据“如果-那么”预测规则预测未来的采购情况，最后利用修正的遗传算法生成最优的先行发货计划；这种遗传算法考虑了其在运输成本和运输距离之外，还有预测规则的置信度，利用大量的数值实验权衡了先行发货中的不同因素，验证了模型的最优可靠性     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Markov chain Monte Carlo methods for high dimensional inversion in remote sensing

Summary.  We discuss the inversion of the gas profiles (ozone, NO₃, NO₂, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the 'Global ozone monitoring of occultation of stars' instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for a more comprehensive statistical inversion are presented. A direct inversion leads to a non-linear model with hundreds of parameters to be estimated. The problem is solved with an adaptive single-step Markov chain Monte Carlo algorithm. Another approach is to divide the problem into several non-linear smaller dimensional problems, to run parallel adaptive Markov chain Monte Carlo chains for them and to solve the gas profiles in repetitive linear steps. The effect of grid size is discussed, and we present how the prior regularization takes the grid size into account in a way that effectively leads to a grid-independent inversion.     

Improving flexibility and efficiency by adding parallelism to genetic algorithms

In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivation is to bring some uniformity to the proposal, comparison, and knowledge exchange among the traditionally opposite kinds of serial and parallel GAs. We comparatively analyze the properties of steady-state, generational, and cellular genetic algorithms. Afterwards, this study is extended to consider a distributed model consisting in a ring of GA islands. The analyzed features are the time complexity, selection pressure, schema processing rates, efficacy in finding an optimum, efficiency, speedup, and resistance to scalability. Besides that, we briefly discuss how the migration policy affects the search. Also, some of the search properties of cellular GAs are investigated. The selected benchmark is a representative subset of problems containing real world difficulties. We often conclude that parallel GAs are numerically better and faster than equivalent sequential GAs. Our aim is to shed some light on the advantages and drawbacks of various sequential and parallel GAs to help researchers using them in the very diverse application fields of the evolutionary computation.     

基于遗传算法与模糊控制的建模方法

从实时系统的需求出发,研究利用遗传算法实现实时模糊控制的新技术,包括规则的形成、前提参数的训练,通过对比不同的训练参数规模,讨论了训练参数规模的大小与模型“范化”之间的关系。 经检验,用遗传算法生成的模糊系统模型输入变量少、规则数目少,并且误差小于多数传统算法生成的模型,整个模型的形成过程时间空间复杂度小、易于控制,且不受经验知识的约束,适用于实时系统。     

Traffic Partition in WDM/SONET Rings to Minimize SONET ADMs

SONET (Synchronous Optical NETworks) add-drop multiplexers (ADMs) are the dominant cost factor in the WDM(Wavelength Division Multiplexing)/SONET rings. The number of SONET ADMs required by a set of traffic streams is determined by the routing and wavelength assignment of the traffic streams. Previous works took as input the traffic streams with routings given a priori and developed various heuristics for wavelength assignment to minimize the SONET ADM costs. However, little was known about the performance guarantees of these heuristics. This paper contributes mainly in two aspects. First, in addition to the traffic streams with pre-specified routing, this paper also studies minimizing the ADM requirement by traffic streams without given routings, a problem which is shown to be NP-hard. Several heuristics for integrated routing and wavelength assignment are proposed to minimize the SONET ADM costs. Second, the approximation ratios of those heuristics for wavelength assignment only and those heuristics for integrated routing and wavelength assignment are analyzed. The new Preprocessed Iterative Matching heuristic has the best approximation ratio: at most 3/2.     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

A simple estimator for a shared frailty regression model

Summary. We propose a simple estimation procedure for a proportional hazards frailty regression model for clustered survival data in which the dependence is generated by a positive stable distribution. Inferences for the frailty parameter can be obtained by using output from Cox regression analyses. The computational burden is substantially less than that of the other approaches to estimation. The large sample behaviour of the estimator is studied and simulations show that the approximations are appropriate for use with realistic sample sizes. The methods are motivated by studies of familial associations in the natural history of diseases. Their practical utility is illustrated with sib pair data from Beaver Dam, Wisconsin.     

延迟支付条件下易逝品的最优订货决策研究

本文在考虑延期支付的情况下构建了二级供应链关于易逝品的最优订货决策模型,其中供应链由单个供应商和多个面临资金约束的零售商组成。在不允许缺货的情况下,供应商为零售商提供延迟支付来缓解零售商的资金压力,同时零售商对产品的定价将随易逝品的价值变质而变化。本文利用遗传算法对模型进行了算例分析,同时对模型中的相关参数进行了敏感性分析。结果表明,延迟支付能够提高供应链总利润并降低风险,从而有利于供应链整体效益的提高。在延期支付条件下,供应链各成员获得的收益增量存在较大差距,其中供应商能够获得更多收益。最后,通过敏感性分析,我们还发现产量及易逝品保质期的变动会对供应链整体利润产生不同程度的影响。