Revenue management: A real options approach

Revenue management is the process of actively managing inventory or capacity to maximize revenues. The active management typically occurs through managerial levers such as price, promotion, or availability. We present a novel real options approach to revenue management that is specifically suited to the car rental business. We illustrate the concept with actual car rental data. The model produces minimally acceptable prices and inventory release quantities (number of cars available for rent at a given price) as a function of remaining time and available inventory. The pricing and inventory release recommendations of the developed model confirm earlier empirical analysis that suggested current practises discount too deeply early in the booking cycle. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

氯化钙对改性聚丙烯酸钠吸湿性能的影响

根据有机高分子吸湿材料与无机盐类吸湿材料在吸湿性能上的互补性,将氯化钙作为改性聚丙烯酸钠的吸湿助剂,研究了二者的共混物在60d的吸湿情况,结果显示：氯化钙可以明显增加改性聚丙烯酸钠的吸湿倍率,使后者的吸湿倍率增加到自重的3倍以上．     

复杂电磁环境下组网雷达探测能力仿真评估研究

为研究复杂电磁环境与组网方式对组网雷达探测能力的影响,构建了复杂电磁环境下雷达探测能力云规则算法模型和探测能力评估模型,然后利用Netlogo仿真平台实现了对3组复杂电磁环境下雷达组网方案探测能力的仿真和评估．仿真结果表明复杂电磁环境对组网雷达探测能力影响明显,通过调整组网方式可以有效提高组网雷达探测能力．     

基于能力的装备型号体系结构开发方法

为适应装备型号需求生成的复杂性,必须开发面向作战能力应用的装备型号体系结构模型。按照装备型号需求生成工程化思想,建立了结构化的装备型号体系结构开发流程,并对每个环节进行了系统分析。装备型号作战体系结构和系统体系结构开发流程都划分为8个环节,为作战活动分析、系统功能分析提供了可追溯、可验证的模块化开发模式。装备型号体系结构关联开发流程分为4个环节,实现了作战能力对系统实体的关联关系,确保了系统需求对作战需求的支持,提高了装备型号需求成果的系统性和正确性。     

概率影响图的防空导弹作战能力影响分析

针对防空导弹系统各作战能力间影响关系难以量化评估的问题,提出利用概率影响图进行分析的方法。在分析防空导弹作战流程、确定各作战活动影响关系的前提下,该方法首先由作战活动影响关系估计各作战能力结点的边缘概率和条件概率;其次利用目标定向法建立防空导弹作战能力概率影响图;最后对概率影响图进行一致性变换,定量评估各作战能力间的影响关系,导找重要能力指标,并动态分析影响关系的概率变化。以战术级爱国者防空导弹系统PAC-3为例借助该方法进行分析。结果证明,利用概率影响图分析防空导弹作战能力影响关系是可行和有效的。     

战场感知和LANCHESTER方程的C~4ISR系统建模

正确利用监视和侦察设备的战场感知能力已成为C4ISR系统作战能力倍增的重要因素,首先建立了战场感知能力的量化模型,然后利用改进的Lanchester方程建立了反应战场感知能力的C4ISR系统量化模型,通过仿真实例验证了模型的有效性,说明战场感知能力在现代战争中争取到主动权,大大提高部队的综合战斗能力.     

一体化联合作战对装备保障系统的影响及要求

装备保障信息建设要适应一体化联合作战的需求,就必须弄清一体化联合作战对装备保障系统的影响。运用系统方法,着眼环境对系统的影响,从分析一体化联合作战本质入手,阐述了一体化联合作战对装备保障系统功能、结构、运行和组成等要素的影响及要求,并为未来我军装备保障信息化建设提出了几点应对策略。     

空降作战中空降兵电子对抗力量运用浅析

在科技引领的现代战争中,空降作战面临着高精度、杀伤力大的制导武器的威胁。电子对抗力量作为电子战中的武装力量,为空降兵提供了重要的安全保障。本文研究了空降作战在复杂的电磁环境中面临的电子武器威胁,分析了空降兵电子对抗力量在空降作战中的地位作用,结合空降作战的特点探索空降兵电子对抗力量的运用模式,如通过电子侦察、电子佯动、压制干扰、综合防护等手段为空降作战快速占据进攻优势奠定良好的电磁环境基础。此外,为完善空降兵电子对抗力量的作战结构,本研究提出空降作战中电子对抗作战运用的要求,以充分发挥电子对抗在空降作战中的能力,确保空降作战中制电磁权的优势,提高空降作战的制胜效能。     

Heart analytics: Analytical modeling of cardiovascular care

Cardiovascular diseases (CVDs) are the leading cause of death and disability both in the United States and worldwide. Despite high morbidity, mortality, and cost in the United States and global healthcare systems, cardiovascular care has been understudied in the healthcare operations management literature. In this paper, we identify research opportunities for healthcare operations management scholars to aid in improving cardiovascular care. We focus on three burdensome conditions, including (1) coronary artery disease, (2) stroke, and (3) heart failure, which, collectively, lead to the vast majority of CVD‐caused mortality and disability. For each condition, we characterize a typical patient's journey in receiving cardiovascular care, elucidating key challenges in improving care and outlining research questions for healthcare operations management scholars. We close with a reference to new research opportunities that emerge as artificial intelligence is likely to transform much of cardiovascular care.     

Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

In a master surgery scheduling (MSS) problem, a hospital's operating room (OR) capacity is assigned to different medical specialties. This task is critical since the risk of assigning too much or too little OR time to a specialty is associated with overtime or deficit hours of the staff, deferral or delay of surgeries, and unsatisfied—or even endangered—patients. Most MSS approaches in the literature focus only on the OR while neglecting the impact on downstream units or reflect a simplified version of the real‐world situation. We present the first prediction model for the integrated OR scheduling problem based on machine learning. Our three‐step approach focuses on the intensive care unit (ICU) and reflects elective and urgent patients, inpatients and outpatients, and all possible paths through the hospital. We provide an empirical evaluation of our method with surgery data for Universitätsklinikum Augsburg, a German tertiary care hospital with 1700 beds. We show that our model outperforms a state‐of‐the‐art model by 43% in number of predicted beds. Our model can be used as supporting tool for hospital managers or incorporated in an optimization model. Eventually, we provide guidance to support hospital managers in scheduling surgeries more efficiently.