基于随机动态规划法的IT项目经济评价

传统的项目经济评价方法--TOC和ROI法不能有效地分析IT项目的投资价值,还可能导致错误的投资决策.通过引入随机动态规划法,可导出两类IT项目(购置型项目和自主开发项目)的经济评价模型,给出项目的投资决策临界值解,并进行详细的影响因素敏感性分析.该评价模型放松了金融期权定价模型的假设前提,更符合IT项目的实际情况,从而能准确地评估IT项目中内含的期权价值和投资机会价值.     

Dynamic risk management in petroleum project investment based on a variable precision rough set model

In this paper, an adaptive algorithm is designed for dynamic risk management in petroleum project investment based on a variable precision rough set (VPRS) model. In risk management, at each stage of decision-making, experts are invited to identify risk indices and support the decision-maker in evaluating the risk exposure (RE) of individual projects. The VPRS model is used to mine risk rules and determine the significance of risk indices from RE decision tables. Considering that there are multiple risks involved in any petroleum project investment, we use multi-objective programming to obtain the optimal selection of projects with minimum RE, where the significance of risk indices is assigned to each of the corresponding multi-objective functions as a weight. Moreover, we develop a risk ranking model to measure the degree of risk for individual projects in a portfolio. Finally, a numerical example based on a Chinese petroleum company's investments in overseas projects is presented to illustrate the proposed approach, and then conclusions are drawn.     

Discounting long run average growth in stochastic dynamic programs

Summary. Finding solutions to the Bellman equation often relies on restrictive boundedness assumptions. In this paper we develop a method of proof that allows to dispense with the assumption that returns are bounded from above. In applications our assumptions only imply that long run average (expected) growth is sufficiently discounted, in sharp contrast with classical assumptions either absolutely bounding growth or bounding each period (instead of long run) maximum (instead of average) growth. We discuss our work in relation to the literature and provide several examples. Received: July 26, 2000; revised version: July 10, 2002 RID="*" ID="*" I am specially grateful to Cuong Le Van and to anonymous referee for detecting an error in a previous version of this paper and for suggestions that sensibly improved the paper. Comments and suggestions are also acknowledged to Michele Boldrin, Raouf Boucekkine, Fabrice Collard, Tim Kehoe, Omar Licandro, and Luis Puch. I am also indebted to participants to the III Summer School on Economic Theory held at the Universidade de Vigo, the Macroeconomics Workshop at the Universitat Autò}noma de Barcelona, and the Econometrics Seminar at Tilburg University. Financial support from the Belgian government, under project PAI P4/01, at the IRES-UCL, from a European Marie Curie fellowship, Grant HPMF-CT-1999-00410, at the CEPREMAP, and from IVIE and Spanish Ministerio de Ciencia y Tecnología and FEDER, under project BEC2001-0535, at the Universidad de Alicante, is gratefully acknowledged.     

Optimum allocation in multivariate stratified random sampling: stochastic matrix mathematical programming

The allocation problem for multivariate stratified random sampling as a problem of stochastic matrix integer mathematical programming is considered, minimizing the estimated covariance matrix of estimated means subject to fixed cost or fixed total sample size. With these aims the asymptotic normality of sample covariance matrices for each strata is established. Some alternative approaches are suggested for its solution. An example is solved by applying the proposed techniques.     

Supply network capacity planning for semiconductor manufacturing with uncertain demand and correlation in demand considerations

A semiconductor supply network involves many expensive steps, which have to be executed to serve global markets. The complexity of global capacity planning combined with the large capital expenditures to increase factory capacity makes it important to incorporate optimization methodologies for cost reduction and long-term planning. The typical view of a semiconductor supply network consists of layers for wafer fab, sort, assembly, test and demand centers. We present a two-stage stochastic integer-programming formulation to model a semiconductor supply network. The model makes strategic capacity decisions, (i.e., build factories or outsource) while accounting for the uncertainties in demand for multiple products. We use the model not only to analyze how variability in demand affects the make/buy decisions but also to investigate how the correlation between demands of different products affects these strategic decisions. Finally, we demonstrate the value of incorporating demand uncertainty into a decision-making scheme.     

An optimization approach for managing fresh food quality throughout the supply chain

One of the most challenging tasks in today's food industry is controlling the product quality throughout the food supply chain. In this paper, we integrate food quality in decision-making on production and distribution in a food supply chain. We provide a methodology to model food quality degradation in such a way that it can be integrated in a mixed-integer linear programming model used for production and distribution planning. The resulting model is applied in an illustrative case study, and can be used to design and operate food distribution systems, using both food quality and cost criteria.     

Sheltering network planning and management with a case in the Gulf Coast region

This paper studies sheltering network planning and operations for natural disaster preparedness and responses with a two-stage stochastic program. The preparedness phase decides the locations, capacities and resources of new Permanent Shelters. Under each disaster scenario, both evacuees and resources are distributed to shelters in the response phase. To address the computational burden, the L-shaped algorithm is applied to decompose the problem into the scenario level with linear programs. A case study for hurricanes in the Gulf Coast region of the US is conducted to demonstrate the implementation of the proposed model.     

A decision method for supplier selection in multi-service outsourcing

Although supplier selection in multi-service outsourcing is a very important decision problem, research concerning this issue is still relatively scarce. This paper proposes a decision method for selecting a pool of suppliers for the provision of different service process/product elements. It pioneers the use of collaborative utility between partner firms for supplier selection. A multi-objective model is built to select desired suppliers. This model is proved to be NP-hard, so we develop a multi-objective algorithm based on Tabu search for solving it. We then use an example to show the applicability of the proposed model and algorithm. Extensive computational experiments are also conducted to further test the performance of the proposed algorithm.