Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods

Summary.  A stochastic discrete time version of the susceptible–infected–recovered model for infectious diseases is developed. Disease is transmitted within and between communities when infected and susceptible individuals interact. Markov chain Monte Carlo methods are used to make inference about these unobserved populations and the unknown parameters of interest. The algorithm is designed specifically for modelling time series of reported measles cases although it can be adapted for other infectious diseases with permanent immunity. The application to observed measles incidence series motivates extensions to incorporate age structure as well as spatial epidemic coupling between communities.     

电子商务对企业定价决策的影响分析及对策

互联网的普及为买卖双方实现交易提供了便利的同时,网上的价格比较侵蚀了商品的价格底线。文章分析了电子商务对企业定价的冲击,给出了“成本透明”条件下企业定价决策的若干建议。     

带有马尔可夫调制和跳的随机微分延迟方程Euler方法的强相合性

本文主要研究了下列形式的随机微分延迟方程：dX（t） =f（X（t） ,X（t -τ（t） ） ,r（t） ）dt ＋g（X（t） ,X（t -τ（t） ） ,r（t） ）dW（t） ＋h（X（t） ,X（t -τ（t） ）, r（t） ）dN（t） 0≤t≤T.考虑了时间延迟．r（t）为变量，Euler方法数值解；给出并且证明了Euler方法的强相合性定理，即Euler方法数值解均方意义下局部收敛于精确解．     

明清五部朝邑县志编纂比较

明清两代朝邑县志今传较广者,分别为明韩邦靖、王学谟及清王兆鳌、朱廷模、李元春等编纂或主修。五部县志编纂目的 相似,然其编纂体例、内容特色、主导思想及成书情况等却不尽一致。比较它们的异同,从中可以看到这一时期我国古方志编 辑思想、体例变化及其发展历程。     

借贷能力、风险收益与新型农业经营主体经营效率

通过构建一个新型农业经营主体与金融组织讨价还价的理论模型,在纳什议价均衡的基础上分析新型经营主体最优产出水平与其借贷能力、风险收益的关系。基于中国12省的微观调查数据,采用双边随机边界检验,对理论模型进行了实证研究,结果表明：借贷能力不足是新型经营主体经营无效率的主导因素,最终使平均净效率下降了8.13%;正规金融机构和民间借贷均对新型经营主体的经营效率有显著影响,并且民间借贷对经营效率的影响更强;新型经营主体的负责人能力、盈利能力、组织化程度等,与其经营效率呈正相关关系。提出应通过金融体系创新、完善民间借贷法规等途径,降低新型农业经营主体的融资约束,提高其负责人的经营水平,加强新型经营主体的组织化程度。     

An integrated approach for water resources decision making under interactive and compound uncertainties

In recent years, the issue of water allocation among competing users has been of great concern for many countries due to increasing water demand from population growth and economic development. In water management systems, the inherent uncertainties and their potential interactions pose a significant challenge for water managers to identify optimal water-allocation schemes in a complex and uncertain environment. This paper thus proposes a methodology that incorporates optimization techniques and statistical experimental designs within a general framework to address the issues of uncertainty and risk as well as their correlations in a systematic manner. A water resources management problem is used to demonstrate the applicability of the proposed methodology. The results indicate that interval solutions can be generated for the objective function and decision variables, and a number of decision alternatives can be obtained under different policy scenarios. The solutions with different risk levels of constraint violation can help quantify the relationship between the economic objective and the system risk, which is meaningful for supporting risk management. The experimental data obtained from the Taguchi's orthogonal array design are useful for identifying the significant factors affecting the means of total net benefits. Then the findings from the mixed-level factorial experiment can help reveal the latent interactions between those significant factors at different levels and their effects on the modeling response.     

Supplies from developing countries: Optimal order quantities under loss risks

When raw material suppliers of a global supply chain are situated in developing countries, not only will there be long lead times but there could also be a possibility of material losses in transit. The magnitude of the losses will be uncertain and can be significant. We consider the optimization of order quantity decisions in such situations. The long lead times imply that we need to take into account the uncertainty in finished goods demands for which the raw materials are to be ordered. The order quantities have to be optimized carefully as they determine the “real options” that become available later in production plans. Using data from the plywood industry and using stochastic programming we demonstrate a method for solving such problems. The method combines simulation and optimization. An interesting observation we make is that the optimal order quantity of a material need not be monotonic in expected loss of that material. In addition, we offer explanations as to why the expected loss need not be monotonic.     

Managing stochastic demand in an Inventory Routing Problem with transportation procurement

We study an Inventory Routing Problem in which the supplier has a limited production capacity and the stochastic demand of the retailers is satisfied with procurement of transportation services. The aim is to minimize the total expected cost over a planning horizon, given by the sum of the inventory cost at the supplier, the inventory cost at the retailers, the penalty cost for stock-out at the retailers and the transportation cost. First, we show that a policy based just on the average demand can have a total expected cost infinitely worse than the one obtained by taking into account the overall probability distribution of the demand in the decision process. Therefore, we introduce a stochastic dynamic programming formulation of the problem that allows us to find an optimal policy in small size instances. Finally, we design and implement a matheuristic approach, integrating a rollout algorithm and an optimal solution of mixed-integer linear programming models, which is able to solve realistic size problem instances. Computational results allow us to provide managerial insights concerning the management of stochastic demand.     

On vendor part delivery dates in a stochastic assembly system

This paper describes the development of a model for the determination of optimal mean part delivery dates in a stochastic assembly system for the objective of minimizing the expected cost of subassembly and part inventory. Parts are assembled at each station to a subassembly. The part delivery and processing times at assembly stations follow known probability distributions. An approximate solution technique based on the optimization of individual stations in isolation is developed. The approximation applies a correction factor, as a function of the variability in part delivery and processing time, cost parameters and number of stations, to the decisions from the single station solutions to compensate for interdependence between stations. Results indicate that this is an effective approach and yields good near-optimal solutions with very little computational effort. Insights regarding the effect of the type of distribution used, random processing times, variance of the distribution used and cost parameter values on part delivery dates are also reported.     

Finite Sample Performance in Cointegration Analysis of Nonlinear Time Series with Long Memory

Nonlinear functions of multivariate financial time series can exhibit long memory and fractional cointegration. However, tools for analysing these phenomena have principally been justified under assumptions that are invalid in this setting. Determination of asymptotic theory under more plausible assumptions can be complicated and lengthy. We discuss these issues and present a Monte Carlo study, showing that asymptotic theory should not necessarily be expected to provide a good approximation to finite-sample behavior.