投资决策分析方法的缺陷及改进

本文指出了传统投资决策方法的缺陷 ,提出了将期权理论应用于投资决策的总体思路 ,突破了传统决策分析的局限性 ,使决策更加科学和合理     

Modelling recruitment training in mathematical human resource planning

This paper deals with mathematical human resource planning; more specifically, it suggests a new model for a manpower‐planning system. In general, we study a k‐classed hierarchical system where the workforce demand at each time period is satisfied through internal mobility and recruitment. The motivation for this work is based on various European Union incentives, which promote regional or local government assistance programs that could be exploited by firms not only for hiring and training newcomers, but also to improve the skills and knowledge of their existing personnel. In this respect, in our augmented mobility model we establish a new ‘training/standby’ class, which serves as a manpower inventory position for potential recruits. This class, which may very well be internal or external to the system, is incorporated into the framework of a non‐homogeneous Markov chain model. Furthermore, cost objectives are employed using the goal‐programming approach, under different operating assumptions, in order to minimize the operational cost in the presence of system's constraints and regulations. Copyright © 2002 John Wiley & Sons, Ltd.     

A Genetic Search for Exploiting a Fuzzy Preference Model of Portfolio Problems with Public Projects

When an organization solves a portfolio problem with public projects evaluated by multiple criteria, in which the economic dimension is not essential or not well characterized, the classical methods are not useful. We propose a non-linear preference model developed from normative Value Theory and using fuzzy sets to model some sources of imprecision. This model can be considered as a generalization of the classical approaches. However, the optimization problem is very complex in order to be solved with non-linear programming techniques. Therefore, the model is exploited by an evolutionary algorithm, able to achieve a strong improvement of the quality of solution.     

Calibrating software reliability models when the test environment does not match the user environment

Software failures have become the major factor that brings the system down or causes a degradation in the quality of service. For many applications, estimating the software failure rate from a user's perspective helps the development team evaluate the reliability of the software and determine the release time properly. Traditionally, software reliability growth models are applied to system test data with the hope of estimating the software failure rate in the field. Given the aggressive nature by which the software is exercised during system test, as well as unavoidable differences between the test environment and the field environment, the resulting estimate of the failure rate will not typically reflect the user‐perceived failure rate in the field. The goal of this work is to quantify the mismatch between the system test environment and the field environment. A calibration factor is proposed to map the failure rate estimated from the system test data to the failure rate that will be observed in the field. Non‐homogeneous Poisson process models are utilized to estimate the software failure rate in both the system test phase and the field. For projects that have only system test data, use of the calibration factor provides an estimate of the field failure rate that would otherwise be unavailable. For projects that have both system test data and previous field data, the calibration factor can be explicitly evaluated and used to estimate the field failure rate of future releases as their system test data becomes available. Copyright © 2002 John Wiley & Sons, Ltd.     

Scale Invariance Properties in the Simulated Annealing Algorithm

The Boltzmann distribution used in the steady-state analysis of the simulated annealing algorithm gives rise to several scale invariant properties. Scale invariance is first presented in the context of parallel independent processors and then extended to an abstract form based on lumping states together to form new aggregate states. These lumped or aggregate states possess all of the mathematical characteristics, forms and relationships of states (solutions) in the original problem in both first and second moments. These scale invariance properties therefore permit new ways of relating objective function values, conditional expectation values, stationary probabilities, rates of change of stationary probabilities and conditional variances. Such properties therefore provide potential applications in analysis, statistical inference and optimization. Directions for future research that take advantage of scale invariance are also discussed.     

Toward Fuzzy Optimization without Mathematical Ambiguity

Fuzzy programming has been discussed widely in literature and applied in such various disciplines as operations research, economic management, business administration, and engineering. The main purpose of this paper is to present a brief review on fuzzy programming models, and classify them into three broad classes: expected value model, chance-constrained programming and dependent-chance programming. In order to solve general fuzzy programming models, a hybrid intelligent algorithm is also documented. Finally, some related topics are discussed.     

Non-universal critical behaviour of heteronuclear dimers on a square lattice––A Monte Carlo study

Monte Carlo simulation within the grand canonical ensemble, the histogram reweighting technique, and finite size scaling analysis are used to explore the phase behaviour of heteronuclear dimers, composed of A and B type atoms, on a square lattice. We have found that for the models with attractive BB and AB nearest-neighbour energy, u_BB=u_AB=−1, and for non-repulsive energy between AA nearest-neighbour sites, u_AA<0, the system belongs to the universality class of the two-dimensional Ising model. However, when u_AA>0, the system exhibits a non-universal critical behaviour. We have evaluated the dependences of the critical point characteristics on the value of u_AA.     

In situ neutron diffraction study (300-1273 K) of non-stoichiometric strontium ferrite SrFeOx

The high-temperature cubic phase of non-stoichiometric strontium ferrite SrFeO_x (2.5≤x≤3.0) has been studied by in situ neutron powder diffraction in air over the temperature range 300-1273 K. The composition of SrFeO_x changes within the range 2.56≤x≤2.81 from 1273 to 673 K, respectively.Rietveld refinements of the diffraction patterns show that the high-temperature cubic phase of SrFeO_x is consistent with a face-centred Fm3c structure. This structure leads to agreement with previous density measurements. This cell allows the high-temperature structure of SrFeO_x to be described in terms of a solid solution of the composition end members. Cubic SrFeO_x at high temperature is found to closely obey Vegard's law. The density of cubic SrFeO_x is also found to exhibit a linear relationship with composition.     

生长温度对碳纳米管阴极场发射性能的影响

碳纳米管(Carbon Nanotubes,CNTs)场发射平面显示器(Field Emission Display,FED)与其他显示器比较显示了其独特优点,被认为是未来理想的平面显示器之一。碳纳米管阴极作为器件的核心部分,其性能的好坏直接影响显示器的性能。针对30~60英寸(76.2~152.4cm)大屏幕显示器所用的厚膜工艺,即采用丝网印刷法制备了碳纳米管阴极阵列,研究了化学气相沉积法在不同温度下生长的CNTs的场发射电流-电压特性,找到了适合FED用碳纳米管的最佳生长温度。结果表明生长温度越高(750℃),CNTs场发射性能越好。并用荧光粉阳极测试这些CNTs的场发射发光显示效果,验证了上述结论。     

宽带膜厚实时监控过程中膜层折射率的确定方法

为了准确计算出镀膜过程中每层膜的折射率，介绍了实时监控过程中确定膜层折射率的2种方法：一种是由实测的透射比光谱直接反算出膜层的折射率；另一种是用最小二乘法的优化算法实时拟合折射率。试验结果表明：在线反算适合单点监控，所得折射率误差小于2%。然而在实际镀膜过程中,由于宽带内膜层参数误差较大,一般大于25%。为此，采用最小二乘法拟合，即在整个宽光谱范围内采集每个波长点的信息，所得结果误差很小，一般都在2％～5%之间,有时可达到10%,在很大程度上提高了实际镀膜时膜厚监控的精度。