带干扰广义Elang(n)风险过程的破产前最大盈余

本文考虑了索赔时间间距为广义Erlang(n)分布的带干扰更新(Sparre Andersen)风险过程.所用的方法类似于Albrecher,et al.(2005),即将广义Erlang(n)随机变量分解成n个独立的指数随机变量的和.建立了破产前最大盈余所满足的积分-微分方程,讨论了索赔量分布为K<,m>分布时的特殊情形.     

带干扰phaes-type风险模型中的最大盈余及相关分布

本文考虑了索赔时间间距为phase-type分布时带干扰更新风险模型中的破产前最大盈余、破产后赤字的分布,建立了相应的积分-微分方程.最后,讨论了当索赔时间间距为Erlang(2)分布且索赔量满足指数分布时的特殊情形.     

Sparre Andersen风险模型的破产问题(英)

本文主要研究了一类Sparre Andersen模型,其索赔时间间隔的分布为指数分布与Erlang(n) 分布的混合.得到了当初始资金u趋于无穷大时,破产概率ψ(u)的确切表达式和渐近表达式.     

Computing steady-state queueing-time distributions of single-server queues:GI X /M/1

This paper presents a computationally efficient method to find the steady-state distributions of actual queueing times of the first customer, as well as of a randomly selected customer, of an arrival group for the queueing systemGI ^X /M/1, and hence the queueing-time distribution of a customer for the systemGI/E _X /1. The distribution of virtual queueing time is also obtained. Approximate analysis based on one or more roots is also discussed. Though the exact detailed as well as approximate computations for a variety of interarrival-time distributions such as generalized Erlang, mixed generalized Erlang, hyperexponential, generalized hyperexponential, and deterministic have been carried out, only representative results in the form of tables have been appended. The results obtained should prove useful to queueing theorists, practitioners, and others.     

Improved approximations for the Erlang loss model

Stochastic loss networks are often very effective models for studying the random dynamics of systems requiring simultaneous resource possession. Given a stochastic network and a multi-class customer workload, the classical Erlang model renders the stationary probability that a customer will be lost due to insufficient capacity for at least one required resource type. Recently a novel family of slice methods has been proposed by Jung et al. (Proceedings of ACM SIGMETRICS conference on measurement and modeling of computer systems, pp. 407–418, 2008) to approximate the stationary loss probabilities in the Erlang model, and has been shown to provide better performance than the classical Erlang fixed point approximation in many regimes of interest. In this paper, we propose some new methods for loss probability calculation. We propose a refinement of the 3-point slice method of Jung et al. (Proceedings of ACM SIGMETRICS conference on measurement and modeling of computer systems, pp. 407–418, 2008) which exhibits improved accuracy, especially when heavily loaded networks are considered, at comparable computational cost. Next we exploit the structure of the stationary distribution to propose randomized algorithms to approximate both the stationary distribution and the loss probabilities. Whereas our refined slice method is exact in a certain scaling regime and is therefore ideally suited to the asymptotic analysis of large networks, the latter algorithms borrow from volume computation methods for convex polytopes to provide approximations for the unscaled network with error bounds as a function of the computational costs.     

Explicit Expressions for the Ruin Probabilities of Erlang Risk Processes with Pareto Individual Claim Distributions

In this paper we first consider a risk process in which claim inter-arrival times and the time untilthe first claim have an Erlang (2) distribution.An explicit solution is derived for the probability of ultimateruin,given an initial reserve of u when the claim size follows a Pareto distribution.Follow Ramsay,Laplacetransforms and exponential integrals are used to derive the solution,which involves a single integral of realvalued functions along the positive real line,and the integrand is not of an oscillating kind.Then we showthat the ultimate ruin probability can be expressed as the sum of expected values of functions of two differentGamma random variables.Finally,the results are extended to the Erlang(n) case.Numerical examples aregiven to illustrate the main results.     

On the stability of retrial queues

We consider the following Type of problems. Calls arrive at a queue of capacity K (which is called the primary queue), and attempt to get served by a single server. If upon arrival, the queue is full and the server is busy, the new arriving call moves into an infinite capacity orbit, from which it makes new attempts to reach the primary queue, until it finds it non-full (or it finds the server idle). If the queue is not full upon arrival, then the call (customer) waits in line, and will be served according to the FIFO order. If λ is the arrival rate (average number per time unit) of calls and μ is one over the expected service time in the facility, it is well known that μ > λ is not always sufficient for stability. The aim of this paper is to provide general conditions under which it is a sufficient condition. In particular, (i) we derive conditions for Harris ergodicity and obtain bounds for the rate of convergence to the steady state and large deviations results, in the case that the inter-arrival times, retrial times and service times are independent i.i.d. sequences and the retrial times are exponentially distributed; (ii) we establish conditions for strong coupling convergence to a stationary regime when either service times are general stationary ergodic (no independence assumption), and inter-arrival and retrial times are i.i.d. exponentially distributed; or when inter-arrival times are general stationary ergodic, and service and retrial times are i.i.d. exponentially distributed; (iii) we obtain conditions for the existence of uniform exponential bounds of the queue length process under some rather broad conditions on the retrial process. We finally present conditions for boundedness in distribution for the case of nonpatient (or non persistent) customers. This revised version was published online in June 2006 with corrections to the Cover Date.     

An Iterative Algorithm for a Multiple Finite-source Queueing Model with Dynamic Priority Scheduling

A multiple finite source queueing model with a single server and dynamic, non-preemptive priority service discipline is studied in this paper. The times the customers spend at the corresponding sources are exponentially distributed. The service times of the customers can follow exponential, Erlang or hyperexponential probability density function. By using results published earlier and an extension of mean value analysis, an iterative algorithm was developed to obtain approximate values of the mean waiting times in queues for the priority classes. The mean number of waiting customers and the server utilization of each class are obtained using the result of this algorithm and Little's formula. The algorithm is preferable to the earlier method, because it does not increase in complexity as the number of customer classes increases.     

A two-echelon base-stock inventory model with Poisson demand and the sequential processing of orders at the upper echelon

We consider a two-echelon inventory system with a number of non-identical, independent ‘retailers’ at the lower echelon and a single ‘supplier’ at the upper echelon. Each retailer experiences Poisson demand and operates a base stock policy with backorders. The supplier manufactures to order and holds no stock. Orders are produced, in first-come first-served sequence, with a fixed production time. The supplier therefore functions as an M/D/1 queue. We are interested in the performance characteristics (average inventory, average backorder level) at each retailer. By finding the distribution of order lead time and hence the distribution of demand during order lead time, we find the steady state inventory and backorder levels based on the assumption that order lead times are independent of demand during order lead time at a retailer. We also propose two alternative approximation procedures based on assumed forms for the order lead time distribution. Finally we provide a derivation of the steady state inventory and backorder levels which will be exact as long as there is no transportation time on orders between the supplier and retailers. A numerical comparison is made between the exact and approximate measures. We conclude by recommending an approach which is intuitive and computationally straightforward.     

Stock Rationing in a Continuous Review Two-Echelon Inventory Model

In this paper we consider a 1-warehouse, N-retailer inventory system where demand occurs at all locations. We introduce an inventory model which allows us to set different service levels for retailers and direct customer demand at the warehouse. For each retailer a critical level is defined, such that a retailer replenishment order is delivered from warehouse stock if and only if the stock level exceeds this critical level. It is assumed that retailer replenishment orders, which are not satisfied from warehouse stock, are delivered directly from the outside supplier, instead of being backlogged. We present an analytical upper bound on the total cost of the system, and develop a heuristic method to optimize the policy parameters. Numerical experiments indicate that our technique provides a very close approximation of the exact cost. Also, we show that differentiating among the retailers and direct customer demand can yield significant cost reductions.     

低碳双渠道供应链的需求信息共享策略研究

在需求不确定环境下,研究考虑消费者低碳意识的双渠道供应链的需求信息共享策略。论文考虑由单个零售商和单个进行碳减排的制造商构成的两级双渠道供应链,构建三种情形的模型:信息不共享、信息共享和只有零售商作预测。通过三种情形下供应链中成员最优决策和利润的比较,得到需求信息共享策略对供应链的影响;并进一步研究消费者低碳意识等参数对供应链运营的影响。研究发现,需求信息共享未必使得供应链成员利润均提高,当制造商预测准确度优于零售商时,零售商会选择信息共享,制造商选择信息不共享情形;当零售商比制造商预测准确时,零售商会选择信息不共享的情形,而制造商会选择仅有零售商预测,此时制造商应采取一定的激励措施以获得零售商的预测信息;消费者低碳意识对批发价、零售价、单位产品减排量、供应链成员利润均产生正向影响。     

低碳双渠道供应链的需求信息共享策略研究

在需求不确定环境下,研究考虑消费者低碳意识的双渠道供应链的需求信息共享策略。论文考虑由单个零售商和单个进行碳减排的制造商构成的两级双渠道供应链,构建三种情形的模型:信息不共享、信息共享和只有零售商作预测。通过三种情形下供应链中成员最优决策和利润的比较,得到需求信息共享策略对供应链的影响;并进一步研究消费者低碳意识等参数对供应链运营的影响。研究发现,需求信息共享未必使得供应链成员利润均提高,当制造商预测准确度优于零售商时,零售商会选择信息共享,制造商选择信息不共享情形;当零售商比制造商预测准确时,零售商会选择信息不共享的情形,而制造商会选择仅有零售商预测,此时制造商应采取一定的激励措施以获得零售商的预测信息;消费者低碳意识对批发价、零售价、单位产品减排量、供应链成员利润均产生正向影响。     

Ergodic theorems for queuing systems with dependent inter-arrival times

We study a G/GI/1 single-server queuing model with i.i.d. service times that are independent of a stationary process of inter-arrival times. We show that the distribution of the waiting time converges to a stationary law as time tends to infinity provided that inter-arrival times satisfy a Gärtner-Ellis type condition. A convergence rate is given and a law of large numbers established. These results provide tools for the statistical analysis of such systems, transcending the standard case with independent inter-arrival times.     

Applications of the Weibull Distribution in Inventory Control

This paper is intended to show that the Weibull distribution can effectively be used to approximate the lead time demand in several cases of distributed demands coupled with distributed lead times. With this approximation, the computations involved are shown to be very simple, requiring no numerical or iterative procedures for determining the re-order levels or protection levels. Some pertinent properties of the Weibull distribution are summarized, and necessary tables for estimating the parameters of the Weibull distribution are given. Numerical examples are given in three cases of distributed demands and lead times and, in each case, the Weibull approximation results are compared with exact and/or other approximate results.     

Order restoration in queues

We examine a class of problems in queueing networks which arise when customers enter a bank of parallel servers in a given order and lose their place in the sequence due to passing resulting from variations in service times. The original sequence is required before service can take place at the next station in the network. These problems arise naturally in flexible manufacturing systems. The result is that the next station beyond the parallel bank of servers can be approximated by a non-Markovian bulk arrival system. This station sees batch arrivals of customers that are eligible to receive service. The size of the arriving batches is shown to depend on the inter-arrival times. Several fundamental results are established for this class of problems including the distribution of batch inter-arrival times, batch sizes, and an approximate method for determining the distribution of the number of eligible and an exact method for determining the distribution of the number of ineligible customers in the system.     

一类Sparre Andersen风险模型的Gerber-Shiu函数

本文考虑了一类相邻两次索赔的时间间隔服从Erlang($n$)和Erlang($m$)的混合分布的Sparre Andersen风险模型.主要目的是研究Gerber-Shiu函数$\phi_\delta(u)$,首先证明了$\phi_\delta(u)$满足一个高阶的积分微分方程,然后讨论了广义Lundberg方程根的性质,在此基础上导出了$\phi_\delta(u)$的拉普拉斯变换并且证明了$\phi_\delta(u)$满足一个更新方程,最后给出了一个例子.     

Coordinating lead times and safety stocks under autocorrelated demand

We consider a supply chain in which orders and lead times are linked endogenously, as opposed to assuming lead times are exogenous. This assumption is relevant when a retailer’s orders are produced by a supplier with finite capacity and replenished when the order is completed. The retailer faces demands that are correlated over time – either positively or negatively – which may, for example, be induced by a pricing or promotion policy. The auto-correlation in demand affects the order stream placed by the retailer onto the supplier, and this in turn influences the resulting lead times seen by the retailer. Since these lead times also determine the retailer’s orders and its safety stocks (which the retailer must set to cover lead time demand), there is a mutual dependency between orders and lead times. The inclusion of endogenous lead times and autocorrelated demand represents a better fit with real-life situations. However, it poses some additional methodological issues, compared to assuming exogenous lead times or stationary demand processes that are independent over time. By means of a Markov chain analysis and matrix analytic methods, we develop a procedure to determine the distribution of lead times and inventories, that takes into account the correlation between orders and lead times. Our analysis shows that negative autocorrelation in demand, although more erratic, improves both lead time and inventory performance relative to IID demand. Positive correlation makes matters worse than IID demand. Due to the endogeneity of lead times, these effects are much more pronounced and substantial error may be incurred if this endogeneity is ignored.     

供应链零售商预测技术研究—基于牛鞭效应的视角

一阶自回归(AR(1))序列模拟需求过程是传统文献采用的经典模型,然而上述文献关于需求过程参数(如需求自回归系数)对牛鞭效应的影响分析缺乏实践意义,为了更符合企业的实际决策过程,本文建立了需求依赖于价格、而以AR(1)序列模拟价格过程的需求函数模型,分析了最小均方差、移动平均和指数平滑预测下的牛鞭效应,确定了零售商的预测技术选择条件。研究表明:(1)产品市场规模不影响零售商预测技术的选择;(2)当产品价格敏感系数较小或价格自回归系数较小时,零售商应选择最小均方差预测技术;(3)当产品价格敏感系数和价格自回归系数均较大时,零售商应选择移动平均预测技术。     

Lack of Invariant Property of the Erlang Loss Model in Case of <Emphasis Type="Italic">MAP</Emphasis> Input

The BMAP/PH/N/0 model with three different disciplines of admission (partial admission, complete rejection, complete admission) is investigated. Loss probability is calculated. Impact of the admission discipline, variation and correlation coefficients of inter-arrival times distribution, and variation of service times distribution on loss probability is analyzed numerically. As by-product, it is shown by means of numerical results that the invariant property of the famous Erlang M/G/N/0 system, which was proven by B. A. Sevastjanov, is absent in case of the MAP input. AMS subject classification: Primary 60K25, 60K20 This revised version was published online in June 2005 with corrected coverdate     

Fitting correlated arrival and service times and related queueing performance

In this paper, we consider a queue where the inter-arrival times are correlated and, additionally, service times are also correlated with inter-arrival times. We show that the resulting model can be interpreted as an MMAP[K]/PH[K]/1 queue for which matrix geometric solution algorithms are available. The major result of this paper is the presentation of approaches to fit the parameters of the model, namely the MMAP, the PH distribution and the parameters introducing correlation between inter-arrival and service times, according to some trace of inter-arrival and corresponding service times. Two different algorithms are presented. The first algorithm is based on available methods to compute a MAP from the inter-arrival times and a PH distribution from the service times. Afterward, the correlation between inter-arrival and service times is integrated by solving a quadratic programming problem over some joint moments. The second algorithm is of the expectation maximization type and computes all parameters of the MAP and the PH distribution in an iterative way. It is shown that both algorithms yield sufficiently accurate results with an acceptable effort.