ACHIEVING NUCLEAR BALANCE

One of the most important questions affecting U.S. national security is the future size of the U.S. nuclear stockpile. While there is clear consensus within the U.S. government on the need to reduce the size of the arsenal, there is none on the best path to achieve these cuts; on the type of deterrent necessary to deal with future threats; or on the size of the production complex needed to support that arsenal. Creating a strategic commission to review these questions, as contemplated in the Fiscal 2008 House Defense Authorization bill, is a necessary first step to establish a sensible nuclear policy. The Reliable Replacement Warhead, which has the potential to transform the complex while preserving the current moratorium on nuclear testing, is a program worth exploring further if it stays within congressionally mandated bounds. As Congress considers both programmatic and policy matters related to U.S. nuclear weapons, it is vital that we also renew and strengthen U.S. leadership on nuclear nonproliferation.     

Route optimization for multiple searchers

We consider a discrete time‐and‐space route‐optimization problem across a finite time horizon in which multiple searchers seek to detect one or more probabilistically moving targets. This article formulates a novel convex mixed‐integer nonlinear program for this problem that generalizes earlier models to situations with multiple targets, searcher deconfliction, and target‐ and location‐dependent search effectiveness. We present two solution approaches, one based on the cutting‐plane method and the other on linearization. These approaches result in the first practical exact algorithms for solving this important problem, which arises broadly in military, rescue, law enforcement, and border patrol operations. The cutting‐plane approach solves many realistically sized problem instances in a few minutes, while existing branch‐and‐bound algorithms fail. A specialized cut improves solution time by 50[percnt] in difficult problem instances. The approach based on linearization, which is applicable in important special cases, may further reduce solution time with one or two orders of magnitude. The solution time for the cutting‐plane approach tends to remain constant as the number of searchers grows. In part, then, we overcome the difficulty that earlier solution methods have with many searchers. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

军事文化:一个范式的转换(节选)

本篇译稿节选了美国空军中校卡伦·O·杜尼文《军事文化——一个范式的转换?》一书的部分内容,作者考察了美国军事文化中的社会性变化,探究了美国军事文化中一种介于军事的传统与非传统之间的,介于男性战士为主的范式或信仰体系和以平等主义与兼容并蓄为特征的充满矛盾的进化军事文化模式之间的斗争。这是一场旧思想和新思想之间的竞赛。译者选编了作者战斗部队中的女性(战争中的女性)这一典型案例来阐述美国军事文化中范式与模式之间的差异。杜尼文中校在本书中还考察了美国军事文化中存在的长期冲突,她建议美国军方现在开始需再一次进行文化范式的转换———从传统的以男性士兵为主的排他性价值观向一种兼容并蓄的从军观念转变。作者强调美国军方必须在其发展过程中,削减其分离主义和排外性的倾向。她提出实施这一范式转换的三个对策(战略部署):改变军方的占主导地位的关于“战斗着的,充满阳刚之气”的具有排他性而非包容性的标准和想法;主动接受并控制正在持续的主要的社会性变化;同时接受与个人和组织对范式转换的职责。范式转换的先驱们必须特别地推崇一种兼容并蓄与平等主义的文化。杜尼文中校也强调了美国高级军事将领是产生美国军事范式转换的最佳催化剂———他们是真正的先驱,他们能够使兼容并蓄的文化范式制度化。杜尼文中校提出如下结论:如果美国希望其军方成为美国社会的一面镜子,无疑要接受一种兼容并蓄的文化范式。     

Note: Facility location with closest rectangular distances

This paper considers the problem of locating one or more new facilities on a continuous plane, where the destinations or customers, and even the facilities, may be represented by areas and not points. The objective is to locate the facilities in order to minimize a sum of transportation costs. What is new in this study is that the relevant distances are the distances from the closest point in the facility to the closest point in the demand areas. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 77–84, 2000     

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     

The Management of Pakistan's Nuclear Arsenal

Pakistan, the fastest growing nuclear weapon state in the world, has established over the last decade a nuclear management system it holds to be “foolproof.” Despite the explosion of radical groups challenging the writ of the state, it dismisses concerns by critics that its nuclear weapons are not safe and secure as “preposterous” and an attempt to “malign” the state. This article examines Pakistan's nuclear management system in four functional areas: command-and-control, physical security, nuclear surety, and doctrine. It describes what is publicly known in each area, identifies areas of omission and inadequacy in each one, and examines several premises of the nuclear program the author considers to be unfounded. Comparing these deficiencies in Pakistan's nuclear management system to the current problems plaguing the US nuclear management system, the author concludes that complacency and unfounded confidence in the efficacy of such programs, if not addressed and corrected, could lead to a future nuclear catastrophe in South Asia.     

A general strategic capacity planning model under demand uncertainty

Capacity planning decisions affect a significant portion of future revenue. In equipment intensive industries, these decisions usually need to be made in the presence of both highly volatile demand and long capacity installation lead times. For a multiple product case, we present a continuous‐time capacity planning model that addresses problems of realistic size and complexity found in current practice. Each product requires specific operations that can be performed by one or more tool groups. We consider a number of capacity allocation policies. We allow tool retirements in addition to purchases because the stochastic demand forecast for each product can be decreasing. We present a cluster‐based heuristic algorithm that can incorporate both variance reduction techniques from the simulation literature and the principles of a generalized maximum flow algorithm from the network optimization literature. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

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.     

Penalty calculations and branching rules in a LAV best subset procedure

Least absolute value (LAV) regression has become a widely accepted alternative to least squares regression. This has come about as the result of advancements in statistical theory and computational procedures to obtain LAV estimates. Computer codes are currently available to solve a wide range of LAV problems including the best subset regression. The purpose of this article is to study the use of penalty calculations and other branching rules in developing the solution tree for the best subset LAV regression.