Doubly Stochastic Sequential Assignment Problem

This article introduces the Doubly Stochastic Sequential Assignment Problem (DSSAP), an extension of the Sequential Stochastic Assignment Problem (SSAP), where sequentially arriving tasks are assigned to workers with random success rates. A given number of tasks arrive sequentially, each with a random value coming from a known distribution. On a task arrival, it must be assigned to one of the available workers, each with a random success rate coming from a known distribution. Optimal assignment policies are proposed for DSSAP under various assumptions on the random success rates. The optimal assignment algorithm for the general case of DSSAP, where workers have distinct success rate distribution, has an exponential running time. An approximation algorithm that achieves a fraction of the maximum total expected reward in a polynomial time is proposed. The results are illustrated by several numerical experiments. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 124–137, 2016     

Impediments to Fighting the Islamic State: Private Contractors and US Strategy

The United States has returned to Iraq, this time to combat the Islamic State. President Barack Obama’s strategy to ‘degrade, and ultimately destroy the terrorist group’ faces serious problems due to political obstacles in obtaining Congressional Authorization for the Use of Military Force (AUMF) and more importantly because of neglect of the crucial role of private contractors. Although the narrative has changed, and there is no public mention of contractors, they remain central to all that the Department of Defense does in the US and abroad. Suggestions are offered on how their performance can be improved to support President Obama’s strategy.     

Capacity expansion and cost efficiency improvement in the warehouse problem

The warehouse problem with deterministic production cost, selling prices, and demand was introduced in the 1950s and there is a renewed interest recently due to its applications in energy storage and arbitrage. In this paper, we consider two extensions of the warehouse problem and develop efficient computational algorithms for finding their optimal solutions. First, we consider a model where the firm can invest in capacity expansion projects for the warehouse while simultaneously making production and sales decisions in each period. We show that this problem can be solved with a computational complexity that is linear in the product of the length of the planning horizon and the number of capacity expansion projects. We then consider a problem in which the firm can invest to improve production cost efficiency while simultaneously making production and sales decisions in each period. The resulting optimization problem is non‐convex with integer decision variables. We show that, under some mild conditions on the cost data, the problem can be solved in linear computational time. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 367–373, 2016     

Economic ordering quantities for recoverable item inventory systems

We study a deterministic EOQ model of an inventory system with items that can be recovered (repaired/refurbished/remanufactured). We use different holding cost rates for manufactured and recovered items, and include disposal. We derive simple square root EOQ formulas for both the manufacturing batch quantity and the recovery batch quantity.     

On the use of lexicographic min cost flows in evacuation modeling

Building evacuation problems can be represented as dynamic network-flow problems [3]. The underlying network structure of a building evolves through time yielding a time-expanded network (a dynamic network). Usually in such evacuation problems involving time, more than one objective function is appropriate. For example, minimizing the total evacuation time and evacuating a portion of the building as early as possible are two such objectives. In this article we show that lexicographical optimization is applicable in handling such multiple objectives. Minimizing the total evacuation time while avoiding cyclic movements in a building and “priority evacuation” are treated as lexicographical min cost flow problems.     

Diffusion approximation to the G/G/R machine repair problem with warm standby spares

The G/G/R machine repair problem with M operating machines, S warm standby spares, and R repairmen is studied as a diffusion process. The steady-state equations are formulated as diffusion equations subject to two reflecting barriers. The approximate diffusion parameters of the diffusion equations are obtained (1) under the assumption that the input characteristics of the problem are defined only by their first two moments rather than their probability distribution function, (2) under the assumption of heavy traffic approximation, that is, when queues of failed machines in the repair stage are almost always nonempty, and (3) using well-known asymptotic results from renewal theory. Expressions for the probability density functions of the number of failed machines in the system are obtained. A study of the derived approximate results, compared to some of the exact results, suggests that the diffusion approach provides a useful method for solving complex machine-repair problems.     

An approach to modeling electric utility capacity expansion planning

This article describes an algorithm for solving the static electric utility capacity expansion problem. The advantages of this algorithm are twofold. First, it is simpler and yet more efficient than previous algorithms for the same problem. Second, by making simplifying but realistic assumptions on plant sizes it is possible to use this algorithm for the case where one does not allow fractional plant additions. While the model has n variables, it has a clear two-dimensional geometric representation for understanding its properties, developing an algorithm, and interpreting the optimal solution. This algorithm has been implemented in the Intermediate Future Forecasting (IFFS) capacity expansion model at the Department of Energy.     

Geopolitics over Proliferation: the Origins of US Grand Strategy and Their Implications for the Spread of Nuclear Weapons in South Asia

How much does the United States care about nonproliferation? Recent scholarship suggests that the fear of spreading nuclear weapons was central to the US grand strategy in the Cold War. In one important case, however, this argument does not hold. This article draws on theoretical debates and newly declassified archives to demonstrate the primacy of geopolitics over nonproliferation in Washington’s policy toward India and Pakistan. Despite their rhetoric, Democratic and Republican leaders consistently relegated nonproliferation to the backburner whenever it conflicted with other strategic goals. Moreover, they inadvertently encouraged proliferation in South Asia at three inter-connected levels: technology, security, and identity.     

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.