Clausewitz and Low-Intensity Conflict

The end of the Cold War led some commentators to question the relevance of Clausewitz's thought in a strategic environment where low-intensity conflict (LIC) would predominate. On the contrary, however, Clausewitz's understanding of the intensity of warfare, and its relationship with the political environment of any given time, makes his thinking compatible with changing historical circumstances. The current War on Terror, for example, can be comfortably accommodated within Clausewitz's concept of war. However, a move away from Clausewitz in US doctrine has been coupled with a rejection of LIC in favour of the erroneous notion of ‘operations other than war’. Consequently, such doctrine lacks the clarity required for its proper dissemination.     

Due-date assignment and early/tardy scheduling on identical parallel machines

This article examines the problem of simultaneously assigning a common due date to a set of independent jobs and scheduling them on identical parallel machines in such a way that the costs associated with the due date and with the earliness or tardiness of the jobs are minimized. We establish that, for certain values of the due-date cost, an optimal schedule for this problem is also optimal for an early/tardy scheduling problem studied by Emmons. We discuss the solution properties for the two problems, and show that both problems are NP-hard even for two machines. We further show that these problems become strongly NP-hard if the number of machines is allowed to be arbitrary. We provide a dynamic programming solution for the problems, the complexity of which indicates that the problems can be solved in pseudopolynomial time as long as the number of machines remains fixed. Finally, we present the results of a limited computational study. © 1994 John Wiley & Sons, Inc.     

On the solution of a stochastic bottleneck assignment problem and its variations

In this article, we study the stochastic version of the so-called bottleneck assignment problem. Our primary objective is to maximize the probability that the bottleneck value satisfies a specified target. Under general stochastic assumptions, we show that the solution in this case is easily obtained by solving a linear assignment problem. We next examine the situation where the target is to be minimized, given that the probability of satisfying the target exceeds a specified threshold. Finally, we address extensions to the original problem where a second objective is also considered.     

Optimal clustering: A model and method

Classifying items into distinct groupings is fundamental in scientific inquiry. The objective of cluster analysis is to assign n objects to up to K mutually exclusive groups while minimizing some measure of dissimilarity among the items. Few mathematical programming approaches have been applied to these problems. Most clustering methods to date only consider lowering the amount of interaction between each observation and the group mean or median. Clustering used in information systems development to determine groupings of modules requires a model that will account for the total group interaction. We formulate a mixed-integer programming model for optimal clustering based upon scaled distance measures to account for this total group interaction. We discuss an efficient, implicit enumeration algorithm along with some implementation issues, a method for computing tight bounds for each node in the solution tree, and a small example. A computational example problem, taken from the computer-assisted process organization (CAPO) literature, is presented. Detailed computational results indicate that the method is effective for solving this type of cluster analysis problem.     

Landmarks in defense literature

A soldier's Duty By Marshal K. Rokossovsky Progress Publishers, Moscow (1985)     

Strategic capacity decision‐making in a stochastic manufacturing environment using real‐time approximate dynamic programming

In this study, we illustrate a real‐time approximate dynamic programming (RTADP) method for solving multistage capacity decision problems in a stochastic manufacturing environment, by using an exemplary three‐stage manufacturing system with recycle. The system is a moderate size queuing network, which experiences stochastic variations in demand and product yield. The dynamic capacity decision problem is formulated as a Markov decision process (MDP). The proposed RTADP method starts with a set of heuristics and learns a superior quality solution by interacting with the stochastic system via simulation. The curse‐of‐dimensionality associated with DP methods is alleviated by the adoption of several notions including “evolving set of relevant states,” for which the value function table is built and updated, “adaptive action set” for keeping track of attractive action candidates, and “nonparametric k nearest neighbor averager” for value function approximation. The performance of the learned solution is evaluated against (1) an “ideal” solution derived using a mixed integer programming (MIP) formulation, which assumes full knowledge of future realized values of the stochastic variables (2) a myopic heuristic solution, and (3) a sample path based rolling horizon MIP solution. The policy learned through the RTADP method turned out to be superior to polices of 2 and 3. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

Robust capacity planning in semiconductor manufacturing

We present a stochastic programming approach to capacity planning under demand uncertainty in semiconductor manufacturing. Given multiple demand scenarios together with associated probabilities, our aim is to identify a set of tools that is a good compromise for all these scenarios. More precisely, we formulate a mixed‐integer program in which expected value of the unmet demand is minimized subject to capacity and budget constraints. This is a difficult two‐stage stochastic mixed‐integer program which cannot be solved to optimality in a reasonable amount of time. We instead propose a heuristic that can produce near‐optimal solutions. Our heuristic strengthens the linear programming relaxation of the formulation with cutting planes and performs limited enumeration. Analyses of the results in some real‐life situations are also presented. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Military Innovation Studies: Multidisciplinary or Lacking Discipline?1

This article explores the evolution of the field of military innovation studies, critiquing its theoretical foundations and setting out a number of challenges that must be overcome if the field is to fulfil its potential and enhance its contribution to wider disciplinary debates as well as to the practitioner community. Tensions between the main theoretical approaches to military innovation are examined as are the challenges inherent in its increasingly multidisciplinary character. The issue of whether military innovation studies constitutes a field in its own right is addressed before recommendations are made to expand the field’s research agenda, broaden its theoretical base and strengthen its multidisciplinary credibility.     

Book reviews

The Balkanization of the West: The Confluence of Postmodernism and Postcommunism By Stjepan G. Mestrovic, Routledge, London (1994). ISBN: 0-414-08755-4, Price £40.00 hardback, £13.99 paperback.

A Naval History of World War I By Professor Paul G. Halpern, UCL Press, 1994. pp. 624 ISBN 1-85728-295-7. Price £25.00

Arms Watch - SIPRI Report on the First Year of the UN Register of Conventional Arms By Edward J. Laurance, Siemon T. Wezeman and Herbert Wulf, Oxford University Press for SIPRI (1993) ISBN 019-829-1795, Price £25.00 hardback, £16.50 paperback.

A League of Airmen By J. A. Winnefeld, (Ed.), RAND, Santa Monica, ISBN 08330-1503-6, Price $30.00 (Hardcover) $15.00 (Softcover)