基于多媒体辅助的高等数学教学模式探究

在对高等数学教学模式构建的理论基础和价值取向分析的基础上,确立了"双主-双线"的教学模式:即在高等数学教学中,发挥教师主要导向作用,体现学生自主学习的主体地位,以师生共同探究、学生自主建构为主线,以现代多媒体和传统媒体的灵活运用为辅线的教学模式.并以此为基础陈述了模式的实施步骤及遵循的原则.     

潜艇隐蔽攻击研究学术贡献与团队分析

本文着重探讨了世界主要军事强国潜艇隐蔽攻击研究主要学术贡献,团队形成的背景、组成、研究趋向与发展前景。     

论高校教师的素质

教学质量是高校生存和发展的生命线。教学质量高低取决于教师的素质。高校教师必须具备较高的政治素质、业务素质,在实践中不断提高教学水平和科研能力。     

分布式数据库系统的查询优化策略研究

在分析比较分布式数据库系统和集中式数据库系统查询优化目标不同特点的基础上,归纳出分布式数据库系统的查询优化目标,进而提出查询优化的策略,并在举例中重点讨论了操作执行顺序的不同对查询性能的影响。     

基于GA-PSO的目标分配问题研究

防空作战中的目标分配问题属于NP完全问题,在综合考虑火力单元作战效能和防御效能的基础上,引入火力单元综合有利度,建立了一种用于大规模多火力单元对抗多轮次目标的静态目标分配模型,给出了基于GA因子的改进PSO算法,并将算法应用于目标分配模型的求解,通过VC 6.0编程和计算机仿真验证了模型的可行性和算法的有效性.     

BSC信道下线性分组码的差错概率下界分析

针对BSC信道,提出了一种线性分组码的最大似然译码差错概率下界的计算方法.根据最大似然译码算法原理,首先将译码差错概率转化为差错事件的联合概率,基于改进的Dawson-Sankoff界的优化准则,推导出BSC信道下线性分组码差错冗余事件的判决准则,最后得到差错概率下界的计算表达式.该下界只依赖于码字的Hamming重量...     

面向装备作战仿真的弹药调拨供应决策建模方法研究

针对用线性规划方法在解决弹药调拨供应决策过程中存在的弹药调拨供应量的分配与弹药保障原则关联不紧密的问题,采用目标规划理论与方法,引入优先级区分主要作战方向和次要作战方向弹药库的重要程度,在同一优先级上以权重区分一梯队、二梯队及预备队弹药库的弹药保障优先顺序,使得模型的构建过程与弹药保障原则紧密相联。同时,在模型构建过程中,综合考虑了弹药消耗预测量、弹药储备标准和弹药运输时限等因素对弹药调拨供应决策的影响,并从本级弹药储备不足和充足两种情况进行了数值模型及分析,验证了模型的正确性与可靠性,为弹药调拨供应提供了决策支持与理论支撑。     

匈牙利法的末段两层火力反TBM目标分配优化

介绍了匈牙利法解决Assignment Problem的基本原理,对匈牙利法解决指派问题的解题步骤和特殊情况进行了说明。在给出末段两层火力防御TBM的目标分配原则的基础上,阐述了相关概念,改进了匈牙利法运用方式,并建立了目标分配模型。最后通过实例的解算,证明了该方法应用于末段两层火力防御TBM目标分配中的可行性,为末段协同反TBM的目标分配提供了一种有效的算法。     

信息熵与TOPSIS装备保障力量部署方案优化

针对装备保障力量部署方案复杂性和模糊性特点,在分析装备保障力量部署的内容、影响因素及方案优化目的基础上,建立了装备保障力量部署方案评估指标体系,采用信息熵理论来确定评估指标的权重,并提出了基于熵权TOPS IS的装备保障力量部署方案优选方法,使装备保障力量部署方案决策更加科学合理,具有一定的实用性。     

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.