物流服务供应链订单分配优化及其遗传算法

针对物流服务供应链订单分配问题中,物流服务集成商通常会按照所分配的订单价值向分包商收取一定比例交易费用的特点,设定交易费用为交易额的线性函数,构建了新的物流服务供应链订单分配优化混合整数规划模型,其优化目标为最小化交易费用、采购费用、短缺服务与延迟供给的物流能力数量。鉴于问题的NP-hard特性,设计了相应的遗传算法,并结合基于优先权的启发式规则避免了大量非法初始解的出现。实验算例表明所建立的模型能够反映物流服务供应链订单分配过程中的线性交易费用因素,其所设计的算法能够在可接受的时间内获得质量较高的满意解,并且对于大规模订单分配优化问题,遗传算法的求解时间与求解结果要优于LINGO软件。     

模糊神经网络的一种混合递推学习算法

提出一种新型混合递推学习算法,记为FNRPCL 及FNIGLS,这种算法用来调整模糊神经网络中隶属函数的中心和宽度,以及输出层的连接权值     

冷贮备冗余系统可靠度预计的递归算法

基于贮备科系统的特点,运用折方法得出一种用计算机求冷贮备冗余系统可靠度的方法,并将该计算方法用于两个计算实例。     

A Faster and Simpler Recursive Algorithm for the LAPACK Routine DGELS

We present new algorithms for computing the linear least squares solution to overdetermined linear systems and the minimum norm solution to underdetermined linear systems. For both problems, we consider the standard formulation min AX – B _F and the transposed formulation min A ^T X – B _F , i.e, four different problems in all. The functionality of our implementation corresponds to that of the LAPACK routine DGELS. The new implementation is significantly faster and simpler. It outperforms the LAPACK DGELS for all matrix sizes tested. The improvement is usually 50–100% and it is as high as 400%. The four different problems of DGELS are essentially reduced to two, by use of explicit transposition of A. By explicit transposition we avoid computing Householder transformations on vectors with large stride. The QR factorization of block columns of A is performed using a recursive level-3 algorithm. By interleaving updates of B with the factorization of A, we reduce the number of floating point operations performed for the linear least squares problem. By avoiding redundant computations in the update of B we reduce the work needed to compute the minimum norm solution. Finally, we outline fully recursive algorithms for the four problems of DGELS as well as for QR factorization.This revised version was published online in October 2005 with corrections to the Cover Date.     

A Recursive Algorithm for Selling at the Ultimate Maximum in Regime-Switching Models

We propose a recursive algorithm for the numerical computation of the optimal value function \(\inf _{t\le \tau \le T} \mathbb {E} \left [\sup _{0\le s\le T } Y_{s} / Y_{\tau } \left | {\mathcal F}_{t}\right .\right ] \) over the stopping times τ with respect to the filtration of a geometric Brownian motion Y _t with Markovian regime switching. This method allows us to determine the boundary functions of the optimal stopping set when no associated Volterra integral equation is available. It applies in particular when regime-switching drifts have mixed signs, in which case the boundary functions may not be monotone.     

A RECURSIVE ALGORITHM AND ITS CONVERGENCE FOR PARAMETER ESTIMATION OF CONVOLUTION MODEL

In this article, the problem on the estimation of the convolution model parameters is considered. The recursive algorithm for estimating model parameters is introduced from the orthogonal procedure of the data, the convergence of this algorithm is theoretically discussed, and a sufficient condition for the convergence criterion of the orthogonal procedure is given. According to this condition, the recursive algorithm is convergent to model wavelet A- = （1, α1,..., αq）.     

A Recursive Algorithm for Adaptive Estimation and Parameter Change Detection of Time Series Models

The discounted recursive least-squares concept of R. G. Brown's adaptive smoothing is extended to the Box-Jenkins models. The D.R.L.S. algorithm is derived for the autoregressive moving-average models. Two important applications of the D.R.L.S. for parameter change detection and adaptive forecasting are presented.     

A New Multichannel Recursive Least Squares Algorithm for Very Robust and Efficient Adaptive Filtering

In this paper, a new multichannel recursive least squares (MRLS) adaptive algorithm is presented which has a number of very interesting properties. The proposed computational scheme performs adaptive filtering via the use of a finite window, where the burdening past information is dropped directly by means of a generalized inversion lemma; consequently, the proposed algorithm has excellent tracking abilities and very low misjudgment. Moreover, the scheme presented here, due to its particular structure and to the proper choice of mathematical definitions behind it, is very robust; i.e., it is less sensitive in the finite precision numerical error generation and propagation. Also, the new algorithm can be parallelized via a simple technique and its parallel form and, when executed with four processors, is faster than all the already existing schemes that perform both infinite and finite window multichannel adaptive filtering. Finally, due to the particular structure of this scheme and to the intrinsic flexibility in the choice of the window length, the proposed algorithm can act as a full substitute of the infinite window MRLS ones.     

A Recursive Test for P-Matrices

The time complexity for testing whether an n-by-n real matrix is a P-matrix is reduced from O(2ⁿ n ³) to O(2 ⁿ ) by applying recursively a criterion for P-matrices based on Schur complementation. A Matlab program implementing the associated algorithm is provided.     

多元时间序列模型协方差矩阵的递归算法

本文推导了多元时序模型的协方差矩阵与模型参数的关系式，并给出了计算多维时序过程自协方差矩阵的递归算法     

改进的生成树遗传算法及其在物流配送成本优化中的应用

针对物流配送成本优化问题的特点,建立了数学模型,并对基本的生成树遗传算法进行了改进,同时详细阐述了改进的生成树编码的遗传算法在物流配送成本优化问题中的运用等关键技术.最后根据此算法设计了一个物流配送优化系统,解决了一个物流配送成本优化问题,效果良好.此系统对于中小型物流公司设计最优物流策略和降低物流成本有着很大的应用前景.     

A Recursive Construction for 2-Designs

This paper contains two main results: given a symmetric (v, k, ) design, D, and a resolvable design which has the parameters of a residual design of D, there exists a symmetric (dv + 1, v, k) design, where d = (v - k)/(k - ), and d is a prime power; given a symmetric (v, k, ) design, D, and a resolvable design with the parameters of a derived design of D, there exists a 2 - (ek + v, 2k, k) design, where e = k/,and e is a prime power.     

A Recursive Algorithm for Computing the Weighted Moore-Penrose Inverse $A^+_{MN}$

In this paper, we give a recursive algorithm for computing the weighted Moore-Penrose inverse $A^+_{MN}$. This method is a generalization of Greville's method for computing Moore-Penrose inverse $A^+$, and the technique of its proof is new. This method suits the weighted least-squares problem.     

最小费用流问题的一种改进算法

本用顶点表和弧表描述和存储最小费用流的参数，借助SQL语言的优点提出了一种求解最小费用流的简便算法。中提出了前沿节点和含潜弧的概念，并利用这些概念减少了最短路算法的迭代次数和每次迭代的计算量。最后给出了一个算例。     

A Recursive Construction for New Symmetric Designs

We introduce a recursive construction of regular Handamard matrices with row sum 2h for h=±3ⁿ. Whenever q=(2h – 1)² is a prime power, we construct, for every positive integer m, a symmetric designs with parameters (4h²(q^m+1 – 1)/(q – 1), (2h² – h)q^m, (h² – h)q^m).     

逆向物流闭环双层优化模型及算法

目前,资源短缺使得许多企业的生产成本不断增加,一些生产商为了降低成本,开始开展回收再生产.这种回收再生产,不但可以减少生产成本,还可以节约社会资源、减少废弃产品对环境的破坏等.但是,回收废旧产品同样需要成本,有些产品在回收过程中的成本远大于利用原材料来生产的产品的成本. 因此,并不是所有的企业都愿意开展回收工作.在本文将就这一问题,通过建立以政府为上层,企业为下层的双层优化模型,并通过求解该模型给出了政府如何对回收成本不同的企业制定不同的奖、罚政策,才能既使各企业既能主动开展回收再生产,又能自觉将其在生产中对环境等的不良影响降至最低,使企业在满足国家可持续发展的要求下达到最优.     

A Recursive Algorithm for State Dependent GI/M/1/N Queue with Bernoulli-Schedule Vacation

In this paper, we study a renewal input working vacations queue with state dependent services and Bernoulli-schedule vacations. The model is analyzed with single and multiple working vacations. The server goes for exponential working vacation whenever the queue is empty and the vacation rate is state dependent. At the instant of a service completion, the vacation is interrupted and the server resumes a regular busy period with probability 1???q (if there are customers in the queue), or continues the vacation with probability q (0?≤?q?≤?1). We provide a recursive algorithm using the supplementary variable technique to numerically compute the stationary queue length distribution of the system. Finally, using some numerical results, we present the parameter effect on the various performance measures.     

A Recursive Algorithm to Restore Images Based on Robust Estimation of NSHP Autoregressive Models

The objective of this article is to present a new image restoration algorithm. First, each pixel in the image is classified into k categories. Then we assume that the gray levels in each category follow a nonsymmetric half-plane (NSHP) autoregressive model. Robust estimation of the parameters of the model is considered to attenuate the effect of the image contamination on the parameters. In each iteration we will construct a new image using a robustified version of the residuals. The introduction of the classification techniques as a first step of the algorithm reduces considerably the number of parameters to estimate. Hence, the computational time is also reduced because the robust estimations of the parameters are solutions of nonlinear system of equations. Some applications are presented to real synthetic aperture radar (SAR) images to illustrate how our algorithm restores an image in practice.