供应链中数学模型及其算法研究

通过对供应链研究现状的分析,进而对供应链中数学模型进行了深入的探讨。论文采用双层规划模型描述了提出的问题,其中上层规划模型是从生产商的角度出发,使得生产成本最小化;而下层规划模型则是从供应商的角度出发,使得收益最大化。文中对双层规划模型设计了相应的模拟退火算法进行求解。算例结果表明,模型和算法都是有效的,从而为供应链中角色决策提供支持。     

Analysis of intermodal freight from China to Indian Ocean: A goal programming approach

This paper presents an intermodal network optimization model to examine the competitiveness of 36 alternative routings for freight moving from China to and beyond Indian Ocean. The proposed model, which is built upon the principles of goal programming, is able to handle multiple and conflicting objective functions such as minimizing transportation cost, transit time and transit time variability while ensuring flow continuity and transit nodes compatibility among the rail, road, ocean vessel, airplane and inland waterway transports. Transportation time and cost obtained from comprehensive industry sources are then fed into the intermodal transport network connecting two important Chinese origins and four Indian destinations, from which the most attractive routes are identified. In addition, the paper investigates several non-dominated transportation cases through sensitivity analysis tests, and analyzes the potential competitiveness and possible influences of future route developments to current transportation patterns in Asia.     

Container storage and transshipment marine terminals

This study addresses the storage arrangement of transshipment containers on a container yard, in order to carry out efficiently the ship handling operations at a terminal where mega-containerships call. An optimization model is specified to investigate the flow of containers from the mega-containership to feeder ships using intermediate storage at the yard. A heuristic based on the lagrangian relaxation is formulated. The quality of the heuristic approach is tested in a number of experiments. In the experiments, various situations are analyzed with respect to mega-containership arrival rates, some strategies for stack arrangements and terminal layouts.     

基于遗传算法的模糊控制器参数优化

针对非线性量化因子模糊控制器的参数对系统性能影响和参数间的相互制约,提出了一种基于遗传算法的参数整定与优化方法,并进行了仿真研究。仿真结果表明通过该方法寻优的系统具有更好的响应速度和控制精度；当对象结构或参数发生变化时,非线性量化因子模糊控制器可以重新整定参数,以保持良好的控制效果,具有很强的鲁棒性。     

Performance of Garch Models in Forecasting the Volatility of Exchange Rate for Developing Countries

This paper empirically investigates the performance of GARCH model in forecasting the volatility of exchange rate of some developing countries. We apply linear GARCH model and non-linear GARCH model. We fit these two models to some developing countries exchange rate index from January, 1998 to February, 2005. The return series of the developing countries＇ foreign exchange rate are leptokurtic, significantly skew, deviating from normality and volatile clustering as well. We find within-sample and out-of-sample evidence that conditional estimates of non-linear GARCH model outperform the conditional estimations of linear GARCH models. In our comparisons in most of the developing countries, the non-linear GARCH model produce better results than the linear GARCH model tor forecasting the volatility of exchange rate.     

How Public Relations Functions to Help Achieving Organizational Goals——Alzheimer＇s Assays Campaign Case Study

One purpose that public relations serves is to help organizations to achieve their objectives. The development of this understanding can be regarded as a four-stage process： research, planning, programming, and evaluation. These four steps are essential to any effective public relations campaign and they are actually not four independent functions. Each step overlaps the others, and if any one is neglected, the entire process might be affected. Alzheimer＇s Assays campaign illustrates a complete public relations project because it experiences all the four stages. Herein Alzheimer＇s Assays campaign is studied as an example to demonstrate how public relations functions to help achieving organizational goals.     

车辆修理集中化布局的双层规划模型研究

在对车辆修理点布局问题分析的基础上,以规划期内修理点的改扩建费用、运营费用和运输费用之和最小为目标建立上层模型,以运输费用最小为目标建立下层模型,以此建立了车辆修理点布局的双层规划优化模型,并给出了相应的求解算法。模型具有较好的适用性和实用性,为铁路车辆修理点的进一步优化调整奠定了较好的理论基础。     

Using Genetic Algorithms to Estimate and Validate Bioeconomic Models: The Case of the Ibero-atlantic Sardine Fishery

Synopsis The Neo-classical approach to fisheries management is based on designing and applying bioeconomic models. Traditionally, the basic bioeconomic models have used pre-established non-linear functional forms (logistic, Cobb–Douglas) in order to try to reflect the dynamics of the renewable resources under study. This assumption might cause misspecification problems and, in consequence, a loss of predictive ability. In this work we intend to verify if there is a bias motivated by employing the said non-linear parametric perspective. For this purpose, we employ a novel non-linear and non-parametric prediction method, called Genetic Algorithms, and we compare its results with those obtained from the traditional methods.     

PARTIAL HEDGING IN A STOCHASTIC VOLATILITY ENVIRONMENT

We consider the problem of partial hedging of derivative risk in a stochastic volatility environment. It is related to state-dependent utility maximization problems in classical economics. We derive the dual problem from the Legendre transform of the associated Bellman equation and interpret the optimal strategy as the perfect hedging strategy for a modified claim. Under the assumption that volatility is fast mean-reverting and using a singular perturbation analysis, we derive approximate value functions and strategies that are easy to implement and study. The analysis identifies the usual mean historical volatility and the harmonically averaged long-run volatility as important statistics for such optimization problems without further specification of a stochastic volatility model. The approximation can be improved by specifying a model and can be calibrated for the leverage effect from the implied volatility skew. We study the effectiveness of these strategies using simulated stock paths.