一类两层规划问题模糊满意解的遗传算法

在现有两层规划问题求解方法的基础上,提出用浮点数编码的遗传算法求解该问题模糊满意解的新方法．这种方法每次提供给决策者一组近似最优解,通过决策者的比较、评价和选择,在交互过程中得到各决策者都满意的解．该方法不仅可以给决策者提供更多的决策环境信息,而且可以适应决策者偏好的变化,使得决策过程更合理,更符合人的认识过程．     

Packing of Unequal Spheres and Automated Radiosurgical Treatment Planning

We study an optimization problem of packing unequal spheres into a three-dimensional (3D) bounded region in connection with radiosurgical treatment planning. Given an input (R, V, S, L), where R is a 3D bounded region, V a positive integer, S a multiset of spheres, and L a location constraint on spheres, we want to find a packing of R using the minimum number of spheres in S such that the covered volume is at least V; the location constraint L is satisfied; and the number of points on the boundary of R that are touched by spheres is maximized. Such a packing arrangement corresponds to an optimal radiosurgical treatment planning. Finding an optimal solution to the problem, however, is computationally intractable. In particular, we show that this optimization problem and several related problems are NP-hard. Hence, some form of approximations is needed. One approach is to consider a simplified problem under the assumption that spheres of arbitrary (integral) diameters are available with unlimited supply, and there are no location constraints. This approach has met with certain success in medical applications using a dynamic programming algorithm (Bourland and Wu, 1996; Wu, 1996). We propose in this paper an improvement to the algorithm that can greatly reduce its computation cost.     

Performance evaluation based on multiple attributes with nonparametric frontiers

Performance rating and comparison of a group of entities is frequently based on the values of several attributes. Such evaluations are often complicated by the absence of a natural or obvious way to weight the importance of the individual dimensions of the performance. This paper proposes a framework based on nonparametric frontiers to rate and classify entities described by multiple performance attributes into ‘performers’ and ‘underperformers’. The method is equivalent to Data Envelopment Analysis (DEA) with entities defined only by outputs. In the spirit of DEA, the weights for each attribute are selected to maximize each entity’s performance score. This approach, however, results in a new linear program that is more direct and intuitive than traditional DEA formulations. The model can be easily understood and interpreted by practitioners since it conforms better to the practice of evaluating and comparing performance using standard specifications. We illustrate the model’s use with two examples. The first evaluates the performance of employees. The second is an application in manufacturing where multiple quality attributes are used to assess and compare performance of different manufacturing processes.     

多拉格朗日乘子情形下影子价格的判别和计算

非线性优化模型中经常出现多重Lagrange乘子现象,导致决策者错误地计算资源的影子价格.本文针对该问题指出最小欧几里得范数的Lagrange乘子是影子价格.同时,还提出了一个无约束优化模型用以求解最小欧几里得范数 Lagrange乘子,该无约束优化模型可以通过经典的非光滑优化算法求解.最后,本文提出一个基于次梯度的算法求解影子价格,该算法是次线性收敛的,且计算时间与约束条件的个数及自变量的个数线性相关.     

基于多阶段随机规划模型的国债动态积极投资策略

本文提出国债组合投资的多阶段随机规划模型,导出基于未来利率市场不确定信息的具备动态调整特点的国债组合主动投资策略。该模型采用基于利率水平、斜率和曲率"三位一体"的离散情景树刻画未来利率期限结构动态演化过程,其中特别考虑了广义货币供给变动的影响;通过最小化国债组合收益的条件风险价值,对国债组合进行主动动态调整;同时兼顾国债投资安全性、流动性和收益性等要求,实现了国债组合投资管理中利率风险规避和收益能力的有效匹配。实证研究表明,与传统久期配比免疫模型相比,该模型确定的最优策略不仅能够为国债组合提供更强的抵御利率风险能力,而且能够稳步提升其收益空间,为金融机构实现国债投资的主动管理提供决策支持。     

Where Publics May Touch

ABSTRACT

In response to the unique sensory modes of blind and partially sighted communities, tactile and multisensory tours and exhibition components of museum collections are gradually becoming more common in North America and Europe. Such initiatives are often intended to give equal forms of access for diverse participating publics. This article provides an auto-ethnographic report on the tactile tours offered by The National Gallery of Canada (Ottawa) program “Stimulating the Senses,” and gives context for sensorial models at play in Western museums today. Museum strategies that encourage intersensorial awareness and access are also discussed. Attention is given to the performative properties of these tours, and the variety of encounter they encourage between publics. This article also includes accounts of the program by its supervisor and coordinator, as well as from a participant. Key questions this article explores are: do such tours in fact give equal access; what motivates the development of such programs in Canadian institutions; and what outcomes are realized through these programs?     

Ho-Lee利率模型下带有零息票债券的投资-消费模型

在随机利率环境下研究一类带有零息票债券的投资-消费问题,其中假设无风险利率是服从Ho-Lee利率模型的随机过程,且金融市场由一种无风险资产、一种风险资产和一种零息票债券所构成。投资人希望选择一种最优投资-消费策略来最大化其有限时间段内终端财富和累积消费的期望效用。文章应用动态规划原理和变量替换方法得到了幂效用和对数效用下最优投资-消费策略的显示解。算例分析演示了最优投资-消费策略随市场参数的变化而变化的动态行为,并给出了一些经济学涵义。     

Using mathematical programming to compute singlular multivariate normal probablities

This paper describes two new, mathematical programming-based approaches for evaluating general, one- and two-sidedp-variate normal probabilities where the variance-covariance matrix (of arbitrary structure) is singular with rankr(r<pand r and p can be of unlimited dimensions. In both cases, principal components are used to transform the original, ill-definedp-dimensional integral into a well-definedrdimensional integral over a convex polyhedron. The first algorithm that is presented uses linear programming coupled with a Gauss-Legendre quadrature scheme to compute this integral, while the second algorithm uses multi-parametric programming techniques in order to significantly reduce the number of optimization problems that need to be solved. The application of the algorithms is demonstrated and aspects of computational performance are discussed through a number of examples, ranging from a practical problem that arises in chemical engineering to larger, numerical examples.     

Risk inflation of sequential tests controlled by alpha investing

Streaming feature selection is a greedy approach to variable selection that evaluates potential explanatory variables sequentially. It selects significant features as soon as they are discovered rather than testing them all and picking the best one. Because it is so greedy, streaming selection can rapidly explore large collections of features. If significance is defined by an alpha investing protocol, then the rate of false discoveries will be controlled. The focus of attention in variable selection, however, should be on fit rather than hypothesis testing. Little is known, however, about the risk of estimators produced by streaming selection and how the configuration of these estimators influences the risk. To meet these needs, we provide a computational framework based on stochastic dynamic programming that allows fast calculation of the minimax risk of a sequential estimator relative to an alternative. The alternative can be data driven or derived from an oracle. This framework allows us to compute and contrast the risk inflation of sequential estimators derived from various alpha investing rules. We find that a universal investing rule performs well over a variety of models and that estimators allowed to have larger than conventional rates of false discoveries produce generally smaller risk.     

Manufacturing network configuration in supply chains with product recovery

Efficient implementation of product recovery requires appropriate network structures. In this paper, we study the network design problem of a firm that manufactures new products and remanufactures returned products in its facilities. We examine the capacity decisions and expected performance of two alternative manufacturing network configurations when demand and return flows are both uncertain.