Lessons learnt? Cultural transfer and revolutionary wars, 1775–1831

Did participants in small wars in the period 1775–1831 learn from previous or contemporary examples? While this is difficult to prove for participants who left no written records, there is considerable evidence in existing publications by practitioners that they did indeed draw out lessons from recent insurgencies, either from their own experience or from events elsewhere which they studied from afar, especially the Spanish Guerrilla, which had already become legendary. Most authors showed an interest in how to stage insurgencies rather than in how to quell them. Even then, transfer did not come in a package of tactics-cum-values, but in each case in different configurations.     

Conjectures for framing cyberwarfare

Cyberspace has emerged as a potentially new (and unconventional) domain for warfare. Much debate has focused on understanding cyber conflict. The ability to critically analyse this phenomenon is important; however, the nascent nature of cyberwarfare and the complexity of the systems involved create challenges not met by conventional approaches. As a first step, this requires an analytical construct to frame discussions in a way that highlights distinct characteristics of the cyber domain. An approach proposed is one of the postulating conjectures for debate as a way to achieve this and to demonstrate its use, both at the strategic and operational levels. It is suggested that such an approach provides one component of a mature analytical framework for the analysis of cyber across a range of warfare domains.     

Complexity and workload considerations in product mix decisions under the theory of constraints

The literature on the product mix decision (or master production scheduling) under the Theory of Constraints (TOC), which was developed in the past two decades, has addressed this problem as a static operational decision. Consequently, the developed solution techniques do not consider the system's dynamism and the associated challenges arising from the complexity of operations during the implementation of master production schedules. This paper aims to address this gap by developing a new heuristic approach for master production scheduling under the TOC philosophy that considers the main operational factors that influence actual throughput after implementation of the detailed schedule. We examine the validity of the proposed heuristic by comparison to Integer Linear Programming and two heuristics in a wide range of scenarios using simulation modelling. Statistical analyses indicate that the new algorithm leads to significantly enhanced performance during implementation for problems with setup times. The findings show that the bottleneck identification approach in current methods in the TOC literature is not effective and accurate for complex operations in real‐world job shop systems. This study contributes to the literature on master production scheduling and product mix decisions by enhancing the likelihood of achieving anticipated throughput during the implementation of the detailed schedule. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 357–369, 2015     

Selling to strategic and loss‐averse consumers: Stocking,procurement, and product design policies

Motivated by the presence of loss‐averse decision making behavior in practice, this article considers a supply chain consisting of a firm and strategic consumers who possess an S‐shaped loss‐averse utility function. In the model, consumers decide the purchase timing and the firm chooses the inventory level. We find that the loss‐averse consumers' strategic purchasing behavior is determined by their perceived gain and loss from strategic purchase delay, and the given rationing risk. Thus, the firm that is cognizant of this property tailors its inventory stocking policy based on the consumers' loss‐averse behavior such as their perceived values of gain and loss, and their sensitivity to them. We also demonstrate that the firm's equilibrium inventory stocking policy reflects both the economic logic of the traditional newsvendor inventory model, and the loss‐averse behavior of consumers. The equilibrium order quantity is significantly different from those derived from models that assume that the consumers are risk neutral and homogeneous in their valuations. We show that the firm that ignores strategic consumer's loss‐aversion behavior tends to keep an unnecessarily high inventory level that leads to excessive leftovers. Our numerical experiments further reveal that in some extreme cases the firm that ignores strategic consumer's loss‐aversion behavior generates almost 92% more leftovers than the firm that possesses consumers’ loss‐aversion information and takes it into account when making managerial decisions. To mitigate the consumer's forward‐looking behavior, we propose the adoption of the practice of agile supply chain management, which possesses the following attributes: (i) procuring inventory after observing real‐time demand information, (ii) enhanced design (which maintains the current production mix but improves the product performance to a higher level), and (iii) customized design (which maintains the current performance level but increases the variety of the current production line to meet consumers’ specific demands). We show that such a practice can induce the consumer to make early purchases by increasing their rationing risk, increasing the product value, or diversifying the product line. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 435–453, 2015     

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     

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.     

空中突击兵力使用方案的优化方法

空中进攻战役全过程突击兵力使用方案的优化问题 ,是当前国内外亟待研究的重大课题。在提出多时段计划思想的基础上 ,给出了一种较为有效的优化模型 ,并探讨了有关的数据过滤问题