误差函数计算方法的研究

误差函数已有多种计算方法，其中按e^-t^2的幂级数展开式为基础的算法，数学上是收敛的．且在科技应用范围内，数值上也是收敛的．数值积分法，如梯形法是计算误差函数更好的方法，文中给出了控制积分变量等分数目的计算公式，并得到了很好的计算结果．     

水库移民最低生活保障线测算研究

水库移民在搬迁后面临着极大的社会经济风险,所以构建水库移民的最低生活保障体系是移民社会经济系统重建的重要环节,而水库移民最低生活保障线的测算是关键的部分。笔者从测算原则入手,建立了水库移民最低生活保障线测算的指标体系和数学模型,并结合算例进行了计算。     

集成化供应链绩效评价方法研究

提出了一种基于客户服务水平的集成化供应链绩效评价的区间数线性规划方法。通过引入系数a,将区间数线性规划问题转化为参数线性规划问题,使评价方法能够反映实现客户服务承诺效用的大小,其评价过程更具柔性,并通过实例分析了该方法的应用效果。研究结果表明,多客户服务是企业所在的供应链之间竞争的焦点,处于集成化供应链响应市场需求的关键环节,而基于客户服务水平的集成化供应链绩效评价能较准确地反映供应链整体绩效,具有重要的参考价值。     

儿童编程工具发展历程、特征及对未来教育路向的影响

摘 要：从1960年代LOGO的诞生开始,儿童编程经历了准备期、发展期、实施期、停滞期、复兴期五个时期的发展。总体特征是从复杂语法到简单语法、从抽象系统到可视化系统、从文本编程到非文本编程、从软件编程到物理编程。编程工具的不断发展和完善,让儿童学习编程成为可能。随着数字化社会的到来,编程教育将逐渐成为儿童教育中不可或缺的一部分,并将向下延伸到小学和幼儿园,其教育路向很可能朝学校化、普及化、低龄化发展。     

工科高等代数教学中数学直观的培养

数学直观是大学数学思维素质培养的主要目标之一,也是培养大学生数学基础和数学应用能力的关键。从数学教育学和认知心理学的角度,分析了数学思维与数学直观培养的关系,研究了工科高等代数知识对大学生数学直观培养的重要性及基于认知结构的教学方法。并从空间解析几何与高等代数的教学实践出发,通过两个例子说明数学直观对大学生数学思维能力培养的重要性。     

基于区间灰色区间数的多准则决策方法

将灰色模糊数扩展,定义了区间灰色区间数及其距离.针对准则值为区间灰色区间数的信息不完全的多准则决策问题,提出了一种基于目标规划法的灰色模糊多准则决策方法,求得最优权重向量,从而得到各方案的排序.最后给出一个算例说明方法的可行性和有效性.     

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.     

The Value of Weather Information for E‐Commerce Operations

To be efficient, logistics operations in e‐commerce require warehousing and transportation resources to be aligned with sales. Customer orders must be fulfilled with short lead times to ensure high customer satisfaction, and the costly under‐utilization of workers must be avoided. To approach this ideal, forecasting order quantities with high accuracy is essential. Many drivers of online sales, including seasonality, special promotions and public holidays, are well known, and they have been frequently incorporated into forecasting approaches. However, the impact of weather on e‐commerce operations has not been rigorously analyzed. In this study, we integrate weather data into the sales forecasting of the largest European online fashion retailer. We find that sunshine, temperature, and rain have a significant impact on daily sales, particularly in the summer, on weekends, and on days with extreme weather. Using weather forecasts, we have significantly improved sales forecast accuracy. We find that including weather data in the sales forecast model can lead to fewer sales forecast errors, reducing them by, on average, 8.6% to 12.2% and up to 50.6% on summer weekends. In turn, the improvement in sales forecast accuracy has a measurable impact on logistics and warehousing operations. We quantify the value of incorporating weather forecasts in the planning process for the order fulfillment center workforce and show how their incorporation can be leveraged to reduce costs and increase performance. With a perfect information planning scenario, excess costs can be reduced by 11.6% compared with the cost reduction attainable with a baseline model that ignores weather information in workforce planning.     

Dynamic Pricing to Minimize Maximum Regret

We consider a dynamic pricing problem that involves selling a given inventory of a single product over a short, two‐period selling season. There is insufficient time to replenish inventory during this season, hence sales are made entirely from inventory. The demand for the product is a stochastic, nonincreasing function of price. We assume interval uncertainty for demand, that is, knowledge of upper and lower bounds but not a probability distribution, with no correlation between the two periods. We minimize the maximum total regret over the two periods that results from the pricing decisions. We consider a dynamic model where the decision maker chooses the price for each period contingent on the remaining inventory at the beginning of the period, and a static model where the decision maker chooses the prices for both periods at the beginning of the first period. Both models can be solved by a polynomial time algorithm that solves systems of linear inequalities. Our computational study demonstrates that the prices generated by both our models are insensitive to errors in estimating the demand intervals. Our dynamic model outperforms our static model and two classical approaches that do not use demand probability distributions, when evaluated by maximum regret, average relative regret, variability, and risk measures. Further, our dynamic model generates a total expected revenue which closely approximates that of a maximum expected revenue approach which requires demand probability distributions.     

Incorporating Time‐Dose‐Response into Legionella Outbreak Models

A novel method was used to incorporate in vivo host–pathogen dynamics into a new robust outbreak model for legionellosis. Dose‐response and time‐dose‐response (TDR) models were generated for Legionella longbeachae exposure to mice via the intratracheal route using a maximum likelihood estimation approach. The best‐fit TDR model was then incorporated into two L. pneumophila outbreak models: an outbreak that occurred at a spa in Japan, and one that occurred in a Melbourne aquarium. The best‐fit TDR from the murine dosing study was the beta‐Poisson with exponential‐reciprocal dependency model, which had a minimized deviance of 32.9. This model was tested against other incubation distributions in the Japan outbreak, and performed consistently well, with reported deviances ranging from 32 to 35. In the case of the Melbourne outbreak, the exponential model with exponential dependency was tested against non‐time‐dependent distributions to explore the performance of the time‐dependent model with the lowest number of parameters. This model reported low minimized deviances around 8 for the Weibull, gamma, and lognormal exposure distribution cases. This work shows that the incorporation of a time factor into outbreak distributions provides models with acceptable fits that can provide insight into the in vivo dynamics of the host‐pathogen system.