Optimal ordering policies when anticipating parameter changes in EOQ systems

The classical Economic Order Quantity Model requires the parameters of the model to be constant. Some EOQ models allow a single parameter to change with time. We consider EOQ systems in which one or more of the cost or demand parameters will change at some time in the future. The system we examine has two distinct advantages over previous models. One obvious advantage is that a change in any of the costs is likely to affect the demand rate and we allow for this. The second advantage is that often, the times that prices will rise are fairly well known by announcement or previous experience. We present the optimal ordering policy for these inventory systems with anticipated changes and a simple method for computing the optimal policy. For cases where the changes are in the distant future we present a myopic policy that yields costs which are near-optimal. In cases where the changes will occur in the relatively near future the optimal policy is significantly better than the myopic policy.     

Interval estimation of a global optimum for large combinatorial problems

Consider an “intractable” optimization problem for which no efficient solution technique exists. Given a systematic procedure for generating independent heuristic solutions, we seek to obtain interval estimates for the globally optimal solution using statistical inference. In previous work, accurate point estimates have been derived. Determining interval estimates, however, is a considerably more difficult task. In this paper, we develop straightforward procedures which compute confidence intervals efficiently in order to evaluate heuristic solutions and assess deviations from optimality. The strategy presented is applicable to a host of combinatorial optimization problems. The assumptions of our model, along with computational experience, are discussed.     

To Protect and Serve: Civilian Policing and the United Nations Mission in Kosovo

Since the end of the Cold War the process of nation building has become a priority in the attempt to maintain world order and security in the wake of the instability and devastation wrought by failed and war-torn states. The proliferation of nation-building missions has led to their diversification and the need to marshal a vast array of resources in order to attain substantial levels of success. This article deals with a new and important component of peacekeeping and nation-building missions, that of civilian policing. The article outlines the goals of using civilian police officers in nation-building missions and discusses a variety of problems (pragmatic, legal, and cultural) that policing may introduce to such missions. This discussion is focused around the civilian policing component of the United Nations Mission in Kosovo. It delineates the successes and hardships so far experienced during the mission in Kosovo and explores the insights that the mission may provide for similar projects in the future.     

Homeland Security and Intelligence: Can Oil Mix with Water in an Open Society?

The newest US Cabinet department is Homeland Security (DHS). One of the most controversial aspects in the process of developing this entity is the role of intelligence in providing information on the increasing terrorist threat. This article examines the intelligence challenges for homeland security. In doing so, it explores the problems of merging disparate cultures - law enforcement vs intelligence, civil entities vs military, federal vs state and local jurisdictions, and domestic focus vs international perspective. Given the nature of the globalized, asymmetric threat, the need for more and better intelligence sharing is obvious. Terrorists and other criminals do not respect international boundaries; in fact, they exploit them in an effort to prevent effective and timely countermeasures. This demands that robust multinational collaboration and interagency coordination be practiced.     

An adaptive dynamic programming algorithm for a stochastic multiproduct batch dispatch problem

We address the problem of dispatching a vehicle with different product classes. There is a common dispatch cost, but holding costs that vary by product class. The problem exhibits multidimensional state, outcome and action spaces, and as a result is computationally intractable using either discrete dynamic programming methods, or even as a deterministic integer program. We prove a key structural property for the decision function, and exploit this property in the development of continuous value function approximations that form the basis of an approximate dispatch rule. Comparisons on single product‐class problems, where optimal solutions are available, demonstrate solutions that are within a few percent of optimal. The algorithm is then applied to a problem with 100 product classes, and comparisons against a carefully tuned myopic heuristic demonstrate significant improvements. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 742–769, 2003.     

Exact algorithms for scheduling multiple families of jobs on parallel machines

In many practical manufacturing environments, jobs to be processed can be divided into different families such that a setup is required whenever there is a switch from processing a job of one family to another job of a different family. The time for setup could be sequence independent or sequence dependent. We consider two particular scheduling problems relevant to such situations. In both problems, we are given a set of jobs to be processed on a set of identical parallel machines. The objective of the first problem is to minimize total weighted completion time of jobs, and that of the second problem is to minimize weighted number of tardy jobs. We propose column generation based branch and bound exact solution algorithms for the problems. Computational experiments show that the algorithms are capable of solving both problems of medium size to optimality within reasonable computational time. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 823–840, 2003.     

Norms,Military Utility,and the Use/Non-use of Weapons: The Case of Anti-plant and Irritant Agents in the Vietnam War

The role of norms and military utility in the use of weapons is disputed by constructivist and realist scholars. Through an examination of US decision-making regarding anti-plant and irritant agents in the Vietnam War, I advance this debate in three key ways. First, I develop structural realism’s expectations regarding the role of military utility. Second, I demonstrate that social and material factors are at play in our understandings of both ‘norms’ and ‘military utility’, and that both played a role in US decisions. Third, I find that the dominant role – as structural realism expects – was played by military utility.     

Design and estimation for small sample sensitivity testing

In sensitivity testing for the Department of Defense, the high cost of experimental units necessitates the use of small sample sizes and accentuates the importance of design. This article compares five data collection-estimation procedures. Four of these are modifications of the Robbins-Monro method, and the other is the Langlie. The simulation study is designed as a factorial experiment with response function, sample size, initial design point, gate width, and noise as factors. The estimated V₅₀ and its MSE are the responses compared to assess the small sample behavior of each method. Although there is no single clear-cut winner, the Delayed Robbins-Monro (DRM) with maximum likelihood estimation and the Estimated Quantal Response Curve (Wu [21]) are shown to perform well over a broad variety of conditions.     

Multiperiod resource allocation with a smoothing objective

We consider a multiperiod resource allocation problem, where a single resource is allocated over a finite planning horizon of T periods. Resource allocated to one period can be used to satisfy demand of that period or of future periods, but backordering of demand is not allowed. The objective is to allocate the resource as smoothly as possible throughout the planning horizon. We present two models: the first assumes that the allocation decision variables are continuous, whereas the second considers only integer allocations. Applications for such models are found, for example, in subassembly production planning for complex products in a multistage production environment. Efficient algorithms are presented to find optimal allocations for these models at an effort of O(T²). Among all optimal policies for each model, these algorithms find the one that carries the least excess resources throughout the planning horizon. © 1995 John Wiley & Sons, Inc.     

A Question of ‘Government’ Control: Afghanistan DDR Programs Since 2001

Since the fall of the Taliban in late 2001, the Islamic Republic of Afghanistan (GIRoA), the United States, the United Nations, and the International Security Assistance Force (ISAF) have funded and led three different Disarmament, Demobilization, and Reintegration (DDR) programs. Despite a significant investment in time and treasure, all of them have failed to significantly reduce the number of insurgents or arbaki (militia). This article explores why these programs failed despite incorporating ideas from the prominent DDR schools of thought. Utilizing Stathis Kalyvas’ theory of The Logic of Violence in Civil War as a lens, this article argues that GIRoA and ISAF did not have sufficient control of territory to entice insurgents or arbaki to reconcile and/or reintegrate with the government. Further, in areas GIRoA nominally controlled in northern and western Afghanistan, regional powerbrokers who controlled these areas balked at these programs.