基于多重尝试Metropolis算法的非线性DSGE模型贝叶斯推断

本文提出一种改进的多重尝试Metropolis算法,用于非线性动态随机一般均衡模型的贝叶斯参数估计和模型选择。多重尝试策略通过每次迭代抽取多个尝试点的方法来提高算法的混合速率,新方法中提出使用近似的方法提高计算速度,并通过接收概率调整偏差。数值实验表明新方法在相同的计算时间内具有更高的估计效率。最后,本文比较了具有不同货币政策设定的模型对中国经济数据的拟合效果,发现中国数据更加支持具有时变通胀目标的模型。     

Two new robust genetic algorithms for the flowshop scheduling problem

The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic algorithms are not suitable for this hard, combinatorial problem unless hybridization is used. This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known algorithms. The optimization criterion considered is the minimization of the total completion time or makespan (_Cmax$C_{\max }$). We show a robust genetic algorithm and a fast hybrid implementation. These algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed algorithms are very effective and at the same time are easy to implement.     

Bivariate Semi-Markov Process for Counterparty Credit Risk

We consider the problem of constructing an appropriate multivariate model to study counterparty credit risk in the credit rating migration problem. For this financial problem different multivariate Markov chain models were proposed. However, the Markovian assumption may be inappropriate for the study of the dynamics of credit ratings, which typically show non Markovian-like behavior. In this article, we develop a semi-Markov approach to study the counterparty credit risk by defining a new multivariate semi-Markov chain model. Methods are given for computing the transition probabilities, reliability functions and the price of a risky Credit Default Swap.     

GURTs研发应用现状及其产生的内在原因

作为国际农业生物技术研发领域的新近发展——基冈利用限制技术（GURTs）自1998年首次被授予专利以来就引起各方面的强烈争议,特别是能够致使后代种子不育的所谓“终止子”技术,更是遭到国际社会特别是不发达国家的强烈谴责。首先从技术层面对GURTs进行了说明,然后重点从国际组织、各国政府及非政府组织对GURTs的态度和申请专利的情况对该技术的研发应用现状作了分析。最后从私人研发商对利润最大化的无止境追求与知识产权保护的“相对”不足、过高的知识产权保护执行成本和能获得超额利润的预期三方面对GURTs产生的内在原因做了深层次剖析。     

The Construction of Optimal Imcomplete Block Designs When Observations Within a Block are Correlated

An algorithmic method is described for the construction of optimal incomplete block designs when a known correlation structure is assumed for observations from plots in the same block. The method is applicable to a wide class of designs and correlation structures. Some examples are given to illustrate the procedure.     

Resource portfolio planning of make-to-stock products using a constraint programming-based genetic algorithm

The investment on facilities for manufacturing high-tech products requires a large amount of capital. Even though the demands of such products change dramatically, a company is forced to implement some make-to-stock policies apart from a regular make-to-order production, so that the capacity of expensive resources can be highly utilized. The inherent characteristics to be considered include finite budget for investing resources, lump demands of customers, long production horizon, many types of products to mix simultaneously, time value of capital and asset, technology innovation of resources, efficient usage of multiple-function machines, and limited capacity of resources. In addition to revenue gained from products and the salvage/assets of resources, a decision maker also needs to consider costs regarding inventory, backorder, and resource acquisition-related costs through procurement, renting, and transfer. This study thus focuses on the following issues: (i) how to decide on resources portfolio regarding the way and timing of acquisting resources, and (ii) how to allocate resources to various orders in each production period. The goal is to maximize the long-term profit. This study formulates the problem as a non-linear mixed integer mathematical programming model. A constraint programming-based genetic algorithm is developed. It has been demonstrated to solve the problem efficiently.     

基于QSIM算法的舆论主体行为模拟研究

社会舆论是一个复杂系统,而探寻舆论形成过程中社会公众(舆论主体)的群体行为特征和相互作用关系,进而为有效调控社会舆论的导向更是一项复杂的系统工程。本文以我国2003年的SARS事件为例,对初期的舆论形成状态,这里特指谣言肆起阶段,基于QSIM算法对社会公众的行为进行定性推理及分析。社会舆论的定性推理方法将为从定性到定量研究舆论提供一个桥梁、一种视角和有效途径。     

金融市场条件高阶矩风险与动态组合投资

针对传统组合投资理论没有考虑高阶矩风险和静态处理问题两大缺陷,提出多元GARCHSK模型用于衡量时变的高阶矩风险,基于效用函数的Taylor展开推导出带有高阶矩风险的动态组合投资策略,并利用遗传算法进行求解。实证研究表明,中国股市不仅存在高阶矩风险,而且风险具有时变特征;为防范高阶矩风险,投资者需要动态地修正他的资产配置权重。     

The Ordered Cutting Stock Problem

The one‐dimensional cutting stock problem (CSP) is a classic combinatorial optimization problem in which a number of parts of various lengths must be cut from an inventory of standard‐size material. The classic CSP ensures that the total demand for a given part size is met but ignores the fact that parts produced by a given cutting pattern may be destined for different jobs. As a result, applying the classic CSP in a dynamic production environment may result in many jobs being open (or partially complete) at any point in time—requiring significant material handling or sorting operations. This paper identifies and discusses a new type of one‐dimensional CSP, called the ordered CSP, which explicitly restricts to one the number of jobs in a production process that can be open, or in process, at any given point in time. Given the growing emphasis on mass customization in the manufacturing industry, this restriction can help lead to a reduction in both in‐process inventory levels and material handling activities. A formal mathematical formulation is provided for the new CSP model, and its applicability is discussed with respect to a production problem in the custom door and window manufacturing industry. A genetic algorithm (GA) solution approach is then presented, which incorporates a customized heuristic for reducing scrap levels. Several different production scenarios are considered, and computational results are provided that illustrate the ability of the GA‐based approach to significantly decrease the amount of scrap generated in the production process.