一个不同时刻加工成本有差异的单机排序问题

考虑一个单机排序问题:一批工件在零时刻到达可加工,加工时不可中断,在某个给定时间区间外的加工工时将招致额外的加工成本;当时间区间为给定参数时,要求确定一个最优加工序,当时间区间为决策变量时,要求找到一个最优序及最优区间位置, 由此来最小化总额外加工成本．文中对各种区间外单位加工工时之额外成本的情况给出了多项式算法, NP-hardness的证明及伪多项式时间算法．     

广义生灭分支过程存活后代数的分布

考虑一个生物种群生灭分支过程,其中个体繁衍后代的出生率与死亡率均为依赖时间的函数.在通常的(条件)独立性假设条件下,用生成函数方法给出了任意个体在给定时刻仍存活或已死亡条件下其存活后代数的分布,进而给出了个体在已知其"生卒时刻",任意时刻存活后代数的分布.     

随机区间[0,T]上混合泊松过程的相关性质

本文定义出一类新的计数模型随机时间区间[0,T]上的混合泊松过程{Nt,0≤t≤T}并验证了其不再具有平衡性和独立增量性,从而与常见的泊松过程不同.文中给出了n个质点发生时刻S1,…,Sn的条件联合分布和n个点间时间间隔T1,…,Tn的联合分布.在一定适当假设下,文中给出了一个其在排队论中应用较多的封闭性定理.     

保修非更新产品检测和非完美视情维修建模与优化

针对退化服从两阶段故障过程的保修非更新产品，提出了有剩余保修时间阈值的非完美视情维修策略。在该策略下，产品保修期分为两阶段：检测预防维修期和最小维修期。在检测预防维修期内，针对检测时刻系统的状态(正常和缺陷)实施不同的维修策略。在最小维修期内，不进行检测，对发生的故障实施最小维修。以生产商承担的平均保修费用为目标函数，对检测间隔、预防维修水平及剩余保修时间阈值进行了联合优化。通过数值算例及对比分析验证了模型的有效性。     

连续区间数的中位算子及其灵敏度分析

研究决策信息以区间数形式给出的方案决策问题。考虑到区间数非均匀分布的特点,为避免区间极端值在区间数确定中产生较大误差,本文将中位数概念运用到区间数的确定上,提出OIP(Ordered Interval Point)有序中位算子。取单位区间单调函数(BUM函数)为二分之一所表示的值为权重,将区间数确定为一个实数,并研究算子单调性和有界性的初等运算性质。通过比较OIP算子与COWA算子对态度参数和区间长度的反应灵敏度,获得了在一定条件下OIP算子对态度参数反应更稳健,对区间长度反应更灵敏的结论。最后用算例证明该算子的可行性和有效性。     

异步切换下一类带有时变时滞非线性切换系统的指数镇定

研究了在异步切换下一类带有时变时滞非线性切换系统指数镇定问题,其中控制器的切换时刻滞后于系统的切换时刻.构造了与参数相关的LyapunovKrasovskii函数,且在控制器与子系统不匹配区间内增加.利用矩阵不等式和平均驻留时间相结合的方法,建立了时变时滞非线性切换系统在异步切换下指数镇定的充分条件.另外,在异步切换下通过矩阵的特殊变形,最终设计出控制器.最后给出仿真算例验证了所得结果的有效性.     

基于活动方法的地铁通勤者出发时间选择研究北大核心CSCD

考虑通勤者出行与活动之间的关联,建立了基于活动方法的出发时间选择模型,分析了常数和线性边际活动效用下地铁通勤者早高峰上班出发时间决策问题,并得出了模型的均衡性质:常数边际活动效用下的出发率是出发时间的线性函数,而线性边际活动效用下的出发率则是非线性函数.最后,算例验证了理论模型的结论,展示了出发率与相关参数之间的关系,还将基于活动方法的均衡结果与基于出行的均衡结果进行比较.结果表明:基于出行的方法会高估最早出发时间和最晚出发时间,低估出行区间长度,并高估最高出发率.当车内拥挤系数、发车间隔和总通勤人数分别增大时,在最早出发时刻,常数边际活动效用下的通勤者比线性边际活动效用下的通勤者更为敏感,且两种活动效用下的通勤者在最早出发时刻比在最晚出发时刻更为敏感.     

基于活动方法的地铁通勤者出发时间选择研究

考虑通勤者出行与活动之间的关联,建立了基于活动方法的出发时间选择模型,分析了常数和线性边际活动效用下地铁通勤者早高峰上班出发时间决策问题,并得出了模型的均衡性质:常数边际活动效用下的出发率是出发时间的线性函数,而线性边际活动效用下的出发率则是非线性函数.最后,算例验证了理论模型的结论,展示了出发率与相关参数之间的关系,还将基于活动方法的均衡结果与基于出行的均衡结果进行比较.结果表明:基于出行的方法会高估最早出发时间和最晚出发时间,低估出行区间长度,并高估最高出发率.当车内拥挤系数、发车间隔和总通勤人数分别增大时,在最早出发时刻,常数边际活动效用下的通勤者比线性边际活动效用下的通勤者更为敏感,且两种活动效用下的通勤者在最早出发时刻比在最晚出发时刻更为敏感.     

容忍最小收益下变保费率的更新风险模型

经典的风险模型在研究破产概率时,定义的破产时刻为盈余过程首次取负值的时刻,而讨论低于某一限度时的时刻为破产时刻更有实际的意义.本文将这一限度定义为容忍最小收益,首先讨论了容忍最小收益下变保费率的更新风险模型最终的破产概率;并进一步讨论了它在大索赔情形下有限时间内的破产概率的性质.     

区间规划问题的最优性条件

区间规划是带有区间参数的规划问题,是一种更易于求解实际问题的柔性规划。它是确定性优化问题的延伸,有区间线性规划和区间非线性规划两种形式。本文讨论了目标函数是区间函数的区间非线性问题。给出了区间规划问题最优性必要条件的较简单证明方法,并利用LU最优解的概念,在一类广义凸函数－(p,r)－ρ－(η,θ)－不变凸函数定义下讨论了最优性充分条件。     

Minimizing the Number of Machine Idle Intervals with Minimum Makespan in a Flow-Shop

This paper considers a selected sequence in a permutation flow-shop. The objective is to minimize the number of machine idle intervals with minimum makespan (or total production time) for this selected sequence. There are at least two advantages of minimizing the number of machine idle intervals. The first reduces the number of times necessary to restart machines. The second achieves a longer period of idle time for each idle interval, and hence the idle time may be used more efficiently. An integer programming formulation is presented to provide the optimal solution. A heuristic algorithm is also proposed to solve large-sized problems. The heuristic finds the optimal solution for the three-machine case and is found to provide the optimal or near-optimal solution for other cases.     

在固定时间抽样的可变抽样区间的极值控制图

本文根据 Reynolds在固定时间抽样的可变抽样区间 (VSIFT)的 - x控制图 [1 ]的模型设计极值 (ζ)图。规定样本在相等间隔的固定时间点抽取 ,当过程有变化的迹象时 ,允许在两个固定时间之间抽取附加样本。若样本点超过控制限 ,则 VSIFT图同常规图一样发信号。本文计算了 VSIFTζ图的发信号前的平均时间 ,并同固定抽样区间 (FSI)的常规ζ图作比较。极值图不需计算 ,有关集中和分散的信息在一个图上给出 ,且可画上规格限 ,在实践中应用方便。本文设计的 VSIFTζ图能缩短过程失控时间从而减少不合格品数     

Learning temporal nodes Bayesian networks

Temporal Nodes Bayesian Networks (TNBNs) are an alternative to Dynamic Bayesian Networks for temporal reasoning with much simpler and efficient models in some domains. TNBNs are composed of temporal nodes, temporal intervals, and probabilistic dependencies. However, methods for learning this type of models from data have not yet been developed. In this paper, we propose a learning algorithm to obtain the structure and temporal intervals for TNBNs from data. The method consists of three phases: (i) obtain an initial approximation of the intervals, (ii) obtain a structure using a standard algorithm and (iii) refine the intervals for each temporal node based on a clustering algorithm. We evaluated the method with synthetic data from three different TNBNs of different sizes. Our method obtains the best score using a combined measure of interval quality and prediction accuracy, and a competitive structural quality with lower running times, compared to other related algorithms. We also present a real world application of the algorithm with data obtained from a combined cycle power plant in order to diagnose temporal faults.     

Estimating the delay-time distribution of faults in repairable machinery from failure data

This paper considers a repairable machine that may fail or sufferbreakdown many times during the course of its service lifetime,and is inspected for visible faults at intervals. The delay-timeconcept of Christer & Waller provides a means of modellingthe behaviour of the system, and predicting such useful quantitiesas reliability and cost, under various putative inspection regimes. Hitherto, model parameters have been estimated mainly from subjectivedata. In this paper, we show that it is both theoretically andpractically possible to estimate model parameters, and makeuseful predictions, purely from objective data, i.e. the historyof breakdown times and the findings of inspections. Model parameters are fitted by the method of maximum likelihood,and selection of the 'best' model made using the Akaike informationcriterion (AIC). Initially, Monte-Carlo studies were made, andshowed that the p r d u r e did enable unbiased and asymptoticallyaccurate estimates of model parameters to be recovered fromdata. Manual records of inspections and failures of a sampleof hospital infusion pumps were then analysed, and values ofmodel parameters estimated. Tests of fit were derived and carriedout. Finally, the reliability of infusion-pump components underdifferent inspection intervals was derived from the delay-timemodel with 95% confidence limits, as a demonstration that themethod does indeed provide a practical tool for optimizing inspectionpolicies. The practical details of the relevant computations are givenin some detail throughout, to enable other workers to followour procedure.     

Optimal checkpointing intervals of three error detection schemes by a double modular redundancy

This paper considers three checkpointing schemes which combine a double modular redundancy and three types of checkpoints: compare-and-store-checkpoint (CSCP), store-checkpoint (SCP), and compare-checkpoint (CCP). An execution time of a task is divided equally into n intervals, and at the end of each interval, a CSCP is always placed. Further, each CSCP interval is also divided equally into m intervals, and at the end of each interval, either CCP or SCP is placed except the last one. Introducing the overheads of comparison, storage, and retry, the mean execution times to complete a task for three schemes are obtained, using the theory of probability. Optimal checkpointing intervals, which minimize the mean times, are analytically derived, and are numerically computed. Three schemes are compared as numerical examples and the best checkpointing scheme is chosen.     

Ruin probabilities for an insurance company based on some stochastic risk models

The paper is concerned with a stochastic risk model with independent random claims and premiums. Recurrence formulas for the ruin probabilities of an insurance company at times of claim payments are obtained. Both the random premiums and the insurance damages are assumed to be independent and identically distributed. The number of claims and premiums are independent Poisson processes, both of which are independent of the size of premiums and claims. We consider the case when the random premiums and insurance damages are exponentially distributed and the more general case when they are gamma distributed with integer parameters. Based on the probabilities obtained in this paper, it is possible to calculate the ruin probabilities on infinite and finite time intervals. Examples are given.     

The departure process of discrete-time queueing systems with Markovian type inputs

This paper proposes a unified matrix-analytic approach to characterize the output processes of general discrete-time lossless/lossy queueing systems in which time is synchronized/slotted into fixed length intervals called slots. The arrival process can be continuous- or discrete-time Markovian processes. It can be either renewal or non-renewal. The service of a customer commences at the beginning of a slot, consumes a random number of slots, and completes at the end of a later slot. The service times are independent and follow a common and general distribution. Systems with and without server vacations are both treated in this paper. These queueing systems have potential applications in asynchronous transfer mode (ATM) networks, packet radio networks, etc. Since the output process of a node in a queueing network becomes an input process to some node at the next stage, the results of this paper can be used to facilitate end-to-end performance analysis which has attracted more and more attention in the literature. This revised version was published online in June 2006 with corrections to the Cover Date.     

Weibull过程将来第k次失效时间的Bayes预测区间

具Weibull强度函数的非齐次Poisson过程经常被用来分析可修系统的失效模式.基于极大似然估计,Engelhardt ＆ Bain(1978)导出了Weibull过程将来第k次失效时间的经典预测区间.在本文中,我们用无信息联合验前分布,根据Weibull过程的前n次失效时间,给出了建立将来第k次失效时间的Bayes预测区间的方法,并说明了如何应用这些方法。     

Short-term electricity demand forecasting using double seasonal exponential smoothing

This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the demand on the corresponding day of adjacent weeks. There is strong appeal in using a forecasting method that is able to capture both seasonalities. The multiplicative seasonal ARIMA model has been adapted for this purpose. In this paper, we adapt the Holt–Winters exponential smoothing formulation so that it can accommodate two seasonalities. We correct for residual autocorrelation using a simple autoregressive model. The forecasts produced by the new double seasonal Holt–Winters method outperform those from traditional Holt–Winters and from a well-specified multiplicative double seasonal ARIMA model.     

Computing latest starting times of activities in interval-valued networks with minimal time lags

This paper deals with problems of determining possible values of earliest and latest starting times of an activity in networks with minimal time lags and imprecise durations that are represented by means of interval or fuzzy numbers. Although minimal time lags are practical in different projects, former researchers have not considered these problems.After proposing propositions which reduce the search space, a novel polynomial algorithm is presented to compute intervals of possible values of latest starting times in interval-valued networks with minimal time lags. Then, the results are extended to networks with fuzzy durations.