‘Mad Dog?’ Samuel Huntington and the Vietnam War

Harvard professor Samuel P. Huntington has frequently been considered a Vietnam War hawk. His observation that ‘forced-draft urbanization’ might help the United States win the war has come to define his engagement in contemporary strategic debates. This essay argues that both Huntington’s academic work and his private policy advice to the U.S. Government in fact urged a political settlement to the conflict. It argues that in spite of this, Huntington refused to break publicly with the U.S. policy because of his wider concern over what he saw as a crisis of authority in the U.S. foreign policy and governing institutions in the era.     

Theories of Non-Balancing and Russia’s Foreign Policy

The beginning of the twenty-first century has witnessed the emergence of balancing responses to the US hegemony, among which Russia’s foreign policies stand out as corresponding to what is understood as ‘hard balancing.’ Why is the United States being balanced against? This paper categorizes the existing theories of non-balancing into six conditions that together guarantee the absence of balancing and demonstrates that the current unipolar system can satisfy only one of them. This eases the systemic constraints and makes balancing possible. The paper then presents three cases of balancing with reference to President Putin’s foreign policy. It argues that even though in terms of relative military capabilities unipolarity still holds, the emergence of counter-hegemonic balancing is indicative of important changes in the nature of post-Cold War American domination.     

Effective in battle: conceptualizing soldiers’ combat effectiveness*

How do we understand combat effectiveness – soldiers’ performance in battle? Despite the broad consensus that understanding combat effectiveness is important both for scholars and policymakers, there is widespread disagreement about what combat effectiveness is. More specifically, studies of effectiveness tend to focus on either the skill of soldiers in battle, or their will to fight. Yet both skill and will are essential components of an effective fighting force. This article argues that understanding combat effectiveness requires understanding both of these key components of effectiveness. In other words, combat effectiveness requires both the skill and will to engage the enemy in an organized manner. It then demonstrates the usefulness of this conceptualization by applying it to the cases of British, Indian, and Australian forces fighting the Japanese during the Second World War. Only when scholars are talking about the same concept will our understanding of the conditions under which militaries are effective in battle progress. By comparing different units fighting the same opponent under the same material conditions, I demonstrate that units vary both in their combat skill and their will to fight, and that understanding their effectiveness in battle requires analyzing both of these key factors.     

Heterogeneous multitrunking queueing systems with thresholds

Queueing systems with multiple servers are commonly used to model telecommunications systems. But, in general, the service rate of each of the servers is not the same. This fact is indeed true in a communication network where one path (server) may be a terrestrial link and the other (server) a satellite link with its inherent propagation delay. In this article we consider a two-server system where arriving customers are first placed in the queue for the faster server until that queue size reaches a certain threshold, whereupon they are diverted to the slower server. Additional arriving customers are assigned to the slower server until the faster server's queue drops to another lower threshold, at which point arrivals are reassigned to the faster server. We develop an exact mathematical model of the steady-state behavior of each queueing system and a simple analytic approximation.     

Minimizing flow time on a single machine with job classes and setup times

We examine the static sequencing problem of ordering the processing of jobs on a single machine so as to minimize the average weighted flow time. It is assumed that all jobs have zero ready times, and that the jobs are grouped into classes with the property that setup tasks are only required when processing switches from jobs of one class to jobs of another class. The time required for each setup task is given by the sum of a setdown time from the previous class and a setup time for the new class. We show that an algorithm presented in the literature for solving a special case of this problem gives suboptimal solutions. A number of properties of the optimal solution are derived, and their use in algorithms is evaluated. Computational results are presented for both a branch-and-bound procedure and a simpler depth-first search.     

Inventory rotation policies for slow moving items

In this article we present a stochastic model for determining inventory rotation policies for a retail firm which must stock many hundreds of distinctive items having uncertain heterogeneous sales patterns. The model develops explicit decision rules for determining (1) the length of time that an item should remain in inventory before the decision is made on whether or not to rotate the item out of inventory and (2) the minimum sales level necessary for retaining the item in inventory. Two inventory rotation policies are developed, the first of which maximizes cumulative expected sales over a finite planning horizon and the second of which maximizes cumulative expected profit. We also consider the statistical behavior of items having uncertain, discrete, and heterogeneous sales patterns using a two-period prediction methodology where period 1 is used to accumulate information on individual sales rates and this knowledge is then used, in a Bayesian context, to make sales predictions for period 2. This methodology assumes that over an arbitrary time interval sales for each item are Poisson with unknown but stationary mean sales rates and the mean sales rates are distributed gamma across all items. We also report the application of the model to a retail firm which stocks many hundreds of distinctive unframed poster art titles. The application provides some useful insights into the behavior of the model as well as some interesting aspects pertaining to the implementation of the results in a “real-world” situation.     

An optimal critical level policy for inventory systems with two demand classes

Traditional inventory systems treat all demands of a given item equally. This approach is optimal if the penalty costs of all customers are the same, but it is not optimal if the penalty costs are different for different customer classes. Then, demands of customers with high penalty costs must be filled before demands of customers with low penalty costs. A commonly used inventory policy for dealing with demands with different penalty costs is the critical level inventory policy. Under this policy demands with low penalty costs are filled as long as inventory is above a certain critical level. If the inventory reaches the critical level, only demands with high penalty costs are filled and demands with low penalty costs are backordered. In this article, we consider a critical level policy for a periodic review inventory system with two demand classes. Because traditional approaches cannot be used to find the optimal parameters of the policy, we use a multidimensional Markov chain to model the inventory system. We use a sample path approach to prove several properties of this inventory system. Although the cost function is not convex, we can build on these properties to develop an optimization approach that finds the optimal solution. We also present some numerical results. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008