Layers of Experiments with Adaptive Combined Design

In the field of nanofabrication, engineers often face unique challenges in resource‐limited experimental budgets, the sensitive nature of process behavior with respect to controllable variables, and highly demanding tolerance requirements. To effectively overcome these challenges, this article proposes a methodology for a sequential design of experiments through batches of experimental runs, aptly named Layers of Experiments with Adaptive Combined Design (LoE/ACD). In higher layers, where process behavior is less understood, experimental regions cover more design space and data points are more spread out. In lower layers, experimental regions are more focused to improve understanding of process sensitivities in a local, data‐rich environment. The experimental design is a combination of a space‐filling and an optimal design with a tuning parameter that is dependent on the amount of information accumulated over the various layers. The proposed LoE/ACD method is applied to optimize a carbon dioxide (epet‐CO2) assisted deposition process for fabricating silver nanoparticles with pressure and temperature variables. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 127–142, 2015     

Single batch machine scheduling with deliveries

We consider the problem of scheduling a set of n jobs on a single batch machine, where several jobs can be processed simultaneously. Each job j has a processing time p_j and a size s_j. All jobs are available for processing at time 0. The batch machine has a capacity D. Several jobs can be batched together and processed simultaneously, provided that the total size of the jobs in the batch does not exceed D. The processing time of a batch is the largest processing time among all jobs in the batch. There is a single vehicle available for delivery of the finished products to the customer, and the vehicle has capacity K. We assume that K = rD, where and r is an integer. The travel time of the vehicle is T; that is, T is the time from the manufacturer to the customer. Our goal is to find a schedule of the jobs and a delivery plan so that the service span is minimized, where the service span is the time that the last job is delivered to the customer. We show that if the jobs have identical sizes, then we can find a schedule and delivery plan in time such that the service span is minimum. If the jobs have identical processing times, then we can find a schedule and delivery plan in time such that the service span is asymptotically at most 11/9 times the optimal service span. When the jobs have arbitrary processing times and arbitrary sizes, then we can find a schedule and delivery plan in time such that the service span is asymptotically at most twice the optimal service span. We also derive upper bounds of the absolute worst‐case ratios in both cases. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 470–482, 2015     

The long shadow of colonial cartography: Britain and the Sino-Indian war of 1962

ABSTRACT

This article examines British responses to the Sino-Indian border war of 1962. It illustrates how, in the years leading up to the war, Britain’s colonial legacy in the Indian subcontinent saw it drawn reluctantly into a territorial dispute between Asia’s two largest and most powerful nations. It analyses disagreements in Whitehall between the Foreign Office and Commonwealth Relations Office over the relative strength of India and China’s border claims, and assesses how these debates reshaped British regional policy. It argues that the border war was instrumental in transforming Britain’s post-colonial relationship with South Asia. Continuing to filter relations with India through an imperial prism proved unsatisfactory, what followed was a more pragmatic Indo-British association.     

What is the global landpower network and what value might it provide?

US national security guidance, as well as the US Army’s operational experiences since 2001, emphasizes the importance of working closely with partner countries to achieve US strategic objectives. The US Army has introduced the global landpower network (GLN) concept as a means to integrate, sustain and advance the Army’s considerable ongoing efforts to meet US national security guidance. This study develops the GLN concept further, and addresses three questions. What benefits can the GLN provide the Army? What are the essential components of the GLN? What options exist for implementing the GLN concept? By developing the GLN concept, the Army has the opportunity to transition the GLN from an often ad hoc and reactive set of relationships to one that the Army more self-consciously prioritizes and leverages as a resource to meet US strategic objectives.     

The “motivated bias” dilemma in warfare and intelligence

The goal of this article is to challenge the assumption of rationality in the behavior of decision-making units involved in security, defense, intelligence and warfare and to consider the influence of “motivated bias” in such instances. A review of motivational literature within international politics and a discussion of literature applying “motivated biases” to warfare and strategic surprise will offer an alternative view of the primacy of rationality in such decisions.     

Involving the elephant: technical isolation and the role of India in a possible solution to the Iranian nuclear crisis

Apart from North Korea, no state's nuclear program in the twenty-first century has raised more concern to international security than Iran's. While Iran insists that its nuclear program is strictly for civilian purpose in line with Article IV of non-proliferation treaty, the USA and its allies insist that Iran has military intentions and called for sanctions. The failure of sanctions to deter Iran from its nuclear agenda had made many scholars and policy-makers call for a preemptive attack on Iranian nuclear facilities. Situated within this debate, this paper positions itself as an antagonist to the preemptive airstrike option and argues that involving India in a possible nuclear “iron curtain” against Iran – a move known as technical isolation – remains the best option to the current nuclear crises.     

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.     

Equity in genetic newborn screening

State‐level newborn screening allows for early treatment of genetic disorders, which can substantially improve health outcomes for newborns. As the cost of genetic testing decreases, it is becoming an essential part of newborn screening. A genetic disorder can be caused by many mutation variants; therefore, an important decision is to determine which variants to search for (ie, the panel design), under a testing budget. The frequency of variants that cause a disorder and the incidence of the disorder vary by racial/ethnic group. Consequently, it is important to consider equity issues in panel design, so as to reduce disparities among different groups. We study the panel design problem using cystic fibrosis (CF) as a model disorder, considering the trade‐offs between equity and accuracy, under a limited budget. Most states use a genetic test in their CF screening protocol, but panel designs vary, and, due to cost, no state's panel includes all CF‐causing variants. We develop models that design equitable genetic testing panels, and compare them with panels that maximize sensitivity in the general population. Our case study, based on realistic CF data, highlights the value of equitable panels and provides important insight for newborn screening practices.     

Decision support model for effects estimation and proportionality assessment for targeting in cyber operations

Cyber operations are relatively a new phenomenon of the last two decades.During that period,they have increased in number,complexity,and agility,while their design and development have been processes well kept under secrecy.As a consequence,limited data(sets)regarding these incidents are available.Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations(such as attack,target identification and selection,and collateral damage),still methodologies and models are needed in order to plan,execute,and assess them in a responsibly and legally compliant way.Based on these facts,it is the aim of this article to propose a model that i))estimates and classifies the effects of cyber operations,and ii)assesses proportionality in order to support targeting decisions in cyber operations.In order to do that,a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical-military experts.The proposed model was evaluated on two cyber operations use cases in a focus group with four technical-military experts.Both the design and the results of the evaluation are revealed in this article.     

Mechanical properties and thermal conductivity of pristine andfunctionalized carbon nanotube reinforced metallic glass composites:A molecular dynamics approach

This work uses the molecular dynamics approach to study the effects of functionalization of carbon nanotubes (CNTs) on the mechanical properties of Cu64Zr36 metallic glass (MG). Three types of functional groups, carboxylic, vinyl and ester were used. The effect of CNT volume fraction (Vf) and the number of functional groups attached to CNT, on the mechanical properties and thermal conductivity of CNT-MG composites was analysed using Biovia Materials Studio. At lower values of Vf (from 0 to 5％), the per-centage increase in Young's modulus was approximately 66％. As the value of Vf was increased further (from 5 to 12％), the rate of increase in Young's modulus was reduced to 16％. The thermal conductivity was found to increase from 1.52 W/mK at Vf=0％to 5.88 W/mK at Vf=12％, thus giving an increase of approximately 286％. Functionalization of SWCNT reduced the thermal conductivity of the SWCNT-MG composites.