The NATO Response Force: A qualified failure no more?

With much fanfare, NATO declared its rapid reaction force—the NATO Response Force (NRF)—an Initial Operational Capability in 2004. This article addresses four questions: Where did the NRF come from? What does it look like in 2017? What have been the major obstacles for the NRF fulfilling its promises? And where is the NRF likely to go? The article holds two main arguments. First, due to inadequate fill-rates and disagreements as to the force’s operational role, the NRF was for many years a “qualified failure.” The force failed to become the operational tool envisioned by the allies in 2002. While not without effect, it fell hostage to the harsh reality of the expeditionary wars of Iraq and Afghanistan. Second, the NRF is off to a fresh beginning and will likely be considered at least a partial success by the allies in the years to come.     

Comprehensive Prohibition of Nuclear Weapons: An Emerging International Norm?

ABSTRACT

There have been calls for the abolition of nuclear weapons from the day they were invented. Over the last fifteen years, some indications can be found that such calls have been getting louder, among them Barack Obama's famous 2009 speech in Prague. In this article, we investigate if support for a comprehensive norm that would prohibit development, possession, and use of nuclear weapons is really growing. To assess the current status of that norm, we use the model of a “norm life cycle,” developed by Martha Finnemore and Kathryn Sikkink. We then analyze 6,545 diplomatic statements from the review process of the Treaty on the Non-Proliferation of Nuclear Weapons as well as from the UN General Assembly First Committee on Disarmament and International Security, covering the years 2000 to 2013. The evidence shows that a comprehensive prohibition can be considered an emerging international norm that finds growing support among states without nuclear weapons and nuclear weapon states alike. Only a core group of states invoke the norm consistently, however. This leads us to conclude that the “tipping point” of the life cycle, at which adherence to a new norm starts to spread rapidly, has yet to be reached.     

Bounded flexibility in days‐on and days‐off scheduling

This article presents a flexible days‐on and days‐off scheduling problem and develops an exact branch and price (B&P) algorithm to find solutions. The main objective is to minimize the size of the total workforce required to cover time‐varying demand over a planning horizon that may extend up to 12 weeks. A new aspect of the problem is the general restriction that the number of consecutive days on and the number of consecutive days off must each fall within a predefined range. Moreover, the total assignment of working days in the planning horizon cannot exceed some maximum value. In the B&P framework, the master problem is stated as a set covering‐type problem whose columns are generated iteratively by solving one of three different subproblems. The first is an implicit model, the second is a resource constrained shortest path problem, and the third is a dynamic program. Computational experiments using both real‐word and randomly generated data show that workforce reductions up to 66% are possible with highly flexible days‐on and days‐off patterns. When evaluating the performance of the three subproblems, it was found that each yielded equivalent solutions but the dynamic program proved to be significantly more efficient. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 678–701, 2013     

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.