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261.
本文讨论了在大规模并行计算机上实现数据并行程序设计语言的关键问题──分布数组的地址计算问题。文中详细给出了维分布数组的下标地址计算公式,分布数组的内情向量结构以及分布数组映射函数算法,并对有关编译实现技术进行了探讨。 相似文献
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The well‐known generalized assignment problem (GAP) involves the identification of a minimum‐cost assignment of tasks to agents when each agent is constrained by a resource in limited supply. The multi‐resource generalized assignment problem (MRGAP) is the generalization of the GAP in which there are a number of different potentially constraining resources associated with each agent. This paper explores heuristic procedures for the MRGAP. We first define a three‐phase heuristic which seeks to construct a feasible solution to MRGAP and then systematically attempts to improve the solution. We then propose a modification of the heuristic for the MRGAP defined previously by Gavish and Pirkul. The third procedure is a hybrid heuristic that combines the first two heuristics, thus capturing their relative strengths. We discuss extensive computational experience with the heuristics. The hybrid procedure is seen to be extremely effective in solving MRGAPs, generating feasible solutions to more than 99% of the test problems and consistently producing near‐optimal solutions. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 468–483, 2001 相似文献
265.
We study a workforce planning and scheduling problem in which weekly tours of agents must be designed. Our motivation for this study comes from a call center application where agents serve customers in response to incoming phone calls. Similar to many other applications in the services industry, the demand for service in call centers varies significantly within a day and among days of the week. In our model, a weekly tour of an agent consists of five daily shifts and two days off, where daily shifts within a tour may be different from each other. The starting times of any two consecutive shifts, however, may not differ by more than a specified bound. Furthermore, a tour must also satisfy constraints regarding the days off, for example, it may be required that one of the days off is on a weekend day. The objective is to determine a collection of weekly tours that satisfy the demand for agents' services, while minimizing the total labor cost of the workforce. We describe an integer programming model where a weekly tour is obtained by combining seven daily shift scheduling models and days‐off constraints in a network flow framework. The model is flexible and can accommodate different daily models with varying levels of detail. It readily handles different days‐off rules and constraints regarding start time differentials in consecutive days. Computational results are also presented. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 607–624, 2001. 相似文献
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We present a stochastic optimization model for planning capacity expansion under capacity deterioration and demand uncertainty. The paper focuses on the electric sector, although the methodology can be used in other applications. The goals of the model are deciding which energy types must be installed, and when. Another goal is providing an initial generation plan for short periods of the planning horizon that might be adequately modified in real time assuming penalties in the operation cost. Uncertainty is modeled under the assumption that the demand is a random vector. The cost of the risk associated with decisions that may need some tuning in the future is included in the objective function. The proposed scheme to solve the nonlinear stochastic optimization model is Generalized Benders' decomposition. We also exploit the Benders' subproblem structure to solve it efficiently. Computational results for moderate‐size problems are presented along with comparison to a general‐purpose nonlinear optimization package. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48:662–683, 2001 相似文献
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A mathematical model of portfolio optimization is usually represented as a bicriteria optimization problem where a reasonable tradeoff between expected rate of return and risk is sought. In a classical Markowitz model, the risk is measured by a variance, thus resulting in a quadratic programming model. As an alternative, the MAD model was developed by Konno and Yamazaki, where risk is measured by (mean) absolute deviation instead of a variance. The MAD model is computationally attractive, since it is easily transformed into a linear programming problem. An extension to the MAD model proposed in this paper allows us to measure risk using downside deviations, with the ability to penalize larger downside deviations. Hence, it provides for better modeling of risk averse preferences. The resulting m‐MAD model generates efficient solutions with respect to second degree stochastic dominance, while at the same time preserving the simplicity and linearity of the original MAD model. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 185–200, 2001 相似文献
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以模拟导航信号源软件开发为例 ,从面向对象分析设计方法、多线程方法、异常处理等几方面介绍了C ++Builder面向对象编程在多通道实时串行通信系统中应用的一种方法 相似文献
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One of the important features of any software system is its operational profile. This is simply the set of all operations that a software is designed to perform and the occurence probabilities of these operations. We present a new model on optimal software testing such that testing is done sequentially using a set of test cases. There may be failures due to the operations in each of these cases. The model parameters, consisting of testing costs and failure rates, all depend on the cases used and the operations performed. Our aim is to find the optimal testing durations in all of the cases in order to minimize the total expected cost. This problem leads to interesting decision models involving nonlinear programming formulations that possess explicit analytical solutions under reasonable assumptions. © 2000 John Wiley & Sons, Inc., Naval Research Logistics 47: 620–634, 2000 相似文献
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We consider a stochastic counterpart of the well-known earliness-tardiness scheduling problem with a common due date, in which n stochastic jobs are to be processed on a single machine. The processing times of the jobs are independent and normally distributed random variables with known means and known variances that are proportional to the means. The due dates of the jobs are random variables following a common probability distribution. The objective is to minimize the expectation of a weighted combination of the earliness penalty, the tardiness penalty, and the flow-time penalty. One of our main results is that an optimal sequence for the problem must be V-shaped with respect to the mean processing times. Other characterizations of the optimal solution are also established. Two algorithms are proposed, which can generate optimal or near-optimal solutions in pseudopolynomial time. The proposed algorithms are also extended to problems where processing times do not satisfy the assumption in the model above, and are evaluated when processing times follow different probability distributions, including general normal (without the proportional relation between variances and means), uniform, Laplace, and exponential. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44, 531–557, 1997. 相似文献