多维随机大与SNBU多维寿命分布类

本文从多维随机序──随机大入手，定义了多维寿命分布类ＳＮＢＵ，证明了该定义与文［１］中的定义等价，研究了其部分性质。     

一类随机过程的多尺度建模

基于多尺度随机模型具有有效性和高度并行算法这一优势，提出如何用三阶树多尺度模型来表示1-D Reciprocal过程；并如何获得多尺度模型的参数。这就为具有Markov统计特性的信号或过程建立起一般的三阶树多尺度随机模型，为更有效的解决实际问题提供了理沦基础，同时，给出了一类定义在单位区间上的随机过程三阶树多尺度表示的仿真示例。     

Poisson单和—类广义Poisson单的刻划

讨论了Ｐｏｉｓｓｏｎ单和一类广义Ｐｏｉｓｓｏｎ单的基本刻划问题。     

随机Benjamin-Ono 方程初值问题及对河口区域生物分布的应用

该文对具梯度耦合噪声的随机Benjamin-Ono 方程（以下简记为B-O方程）的初值问题导出了解得积分表达式，并将它应用于河口区域生物分布问题，得到了一维空间的分布表达式。     

用遗传算法解决一类模糊流水车间调度问题

对同时考虑模糊加工时间和模糊交货期的模糊流水车间调度问题进行了研究 ,模糊加工时间用三角模糊数来表示 ,模糊交货期用梯形模糊数来表示。应用遗传算法搜索最优排序 ,在适应度函数处理中引入模糊数处理方法 ,以平均满意度最大作为优化目标。最后给出了实验结果和结论     

非线性随机规划问题最优值与最优解的可测性

利用对偶理论与可测集值映射理论等,本文首次证明了一般非线性随机规划问题最优值和最优解的可测性,从而为研究它们的概率分布、矩等分布问题以及一般随机规划问题的实际求解奠定了理论基础.     

减少化验次数的一个优化模型

依据数学理论证明在一定条件下，可以减少化验次数；并给出了选择方法；建立了一个减少化验次数的优化模型。     

FROM MANUFACTURING SCHEDULING TO SUPPLY CHAIN COORDINATION：THE CONTROL OF COMPLEXITY AND UNCERTAINTY

With time-based competition and rapid technology advancements, effective manufacturingscheduling and supply chain coordination are critical to quickly respond to changing marketconditions. These problems, however, are difficult in view of inherent complexity and variousuncertainties involved. Based on a series of results by the authors, decomposition and coordination byusing Lagrangian relaxation is identified in this paper as an effective way to control complexity anduncertainty.A manufacturing scheduling problem is first formulated within the job shop context withuncertain order arrivals, processing times, due dates, and part priorities as a separable optimizationproblem. A solution methodology that combines Lagrangian relaxation, stochastic dynamicprogramming, and heuristics is developed. Method improvements to effectively solve large problemsare also highlighted. To extend manufacturing scheduling within a factory to coordinate autonomicmembers across chains of suppliers, a decentralized supply chai     

SCHEDULING PROBLEMS - AN OVERVIEW

There seems to be a significant gap between the theoretical and the practical aspects of scheduling problems in the job shop environment. Theoretically, scheduling systems are designed on the basis of an optimum approach to the scheduling model. However in the practice, the optimum that is built into the scheduling applications seems to face some challenges when dealing with the dynamic character of a scheduling system, for instance machine breakdown or change of orders. Scheduling systems have become quite complex in the past few years. Competitive business environments and shorter product life cycles are the imminent challenges being faced by many companies these days. These challenges push companies to anticipate a demand driven supply chain in their business environment. A demand-driven supply chain incorporates the customer view into the supply chain processes. As a consequence of this, scheduling as a core process of the demand-driven supply chain must also reflect the customer view. In addition,     

Subset threshold autoregression

We develop in this paper an efficient way to select the best subset threshold autoregressive model. The proposed method uses a stochastic search idea. Differing from most conventional approaches, our method does not require us to fix the delay or the threshold parameters in advance. By adopting the Markov chain Monte Carlo techniques, we can identify the best subset model from a very large of number of possible models, and at the same time estimate the unknown parameters. A simulation experiment shows that the method is very effective. In its application to the US unemployment rate, the stochastic search method successfully selects lag one as the time delay and five best models from more than 4000 choices. Copyright © 2003 John Wiley & Sons, Ltd.