Economic ordering decisions with market choice flexibility

Standard approaches to classical inventory control problems treat satisfying a predefined demand level as a constraint. In many practical contexts, however, total demand is comprised of separate demands from different markets or customers. It is not always clear that constraining a producer to satisfy all markets is an optimal approach. Since the inventory‐related cost of an item depends on total demand volume, no clear method exists for determining a market's profitability a priori, based simply on per unit revenue and cost. Moreover, capacity constraints often limit a producer's ability to meet all demands. This paper presents models to address economic ordering decisions when a producer can choose whether to satisfy multiple markets. These models result in a set of nonlinear binary integer programming problems that, in the uncapacitated case, lend themselves to efficient solution due to their special structure. The capacitated versions can be cast as nonlinear knapsack problems, for which we propose a heuristic solution approach that is asymptotically optimal in the number of markets. The models generalize the classical EOQ and EPQ problems and lead to interesting optimization problems with intuitively appealing solution properties and interesting implications for inventory and pricing management. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

The parallel replacement problem with demand and capital budgeting constraints

A generalized parallel replacement problem is considered with both fixed and variable replacement costs, capital budgeting, and demand constraints. The demand constraints specify that a number of assets, which may vary over time, are required each period over a finite horizon. A deterministic, integer programming formulation is presented as replacement decisions must be integer. However, the linear programming relaxation is shown to have integer extreme points if the economies of scale binary variables are fixed. This allows for the efficient computation of large parallel replacement problems as only a limited number of 0–1 variables are required. Examples are presented to provide insight into replacement rules, such as the “no‐splitting‐rule” from previous research, under various demand scenarios. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 40–56, 2000     

The Just Warrior Ethos: A Response to Colonel Riza

In 2014, Colonel M. Shane Riza published an article in this journal arguing that remotely piloted aircraft (RPA) and robotic weapons threaten the US Air Force’s warrior ethos. Riza has clearly articulated the sentiments of one side of a vibrant debate within our service. This paper presents an alternative view; a view held by some who have experienced these new forms and tools of war, and who have wrestled with their implications first-hand. In this paper, we address some methodological concerns with Riza’s approach and then engage some misunderstandings about RPA’s relationship to military history and to risk. The second part of this paper takes a close look at some of the early Just War thinkers to determine what implications the tradition may have on the warrior ethos. We propose, as an alternative to Riza’s position, a return to an ethos grounded in humility, charity, and a conception of war as a last resort; in short, a return to the Just Warrior Ethos.     

Parallel machine scheduling with batch deliveries to minimize total flow time and delivery cost

Motivated by the flow of products in the iron and steel industry, we study an identical and parallel machine scheduling problem with batch deliveries, where jobs finished on the parallel machines are delivered to customers in batches. Each delivery batch has a capacity and incurs a cost. The objective is to find a coordinated production and delivery schedule that minimizes the total flow time of jobs plus the total delivery cost. This problem is an extension of the problem considered by Hall and Potts, Ann Oper Res 135 (2005) 41–64, who studied a two‐machine problem with an unbounded number of transporters and unbounded delivery capacity. We first provide a dynamic programming algorithm to solve a special case with a given job assignment to the machines. A heuristic algorithm is then presented for the general problem, and its worst‐case performance ratio is analyzed. The computational results show that the heuristic algorithm can generate near‐optimal solutions. Finally, we offer a fully polynomial‐time approximation scheme for a fixed number of machines. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 492–502, 2016     

A Reversal of Fortunes? Extended Deterrence and Assurance in Europe and East Asia1

ABSTRACT

The extended deterrence relationships between the United States and its allies in Europe and East Asia have been critical to regional and global security and stability, as well as to nonproliferation efforts, since the late 1950s. These relationships developed in different regional contexts, and reflect differing cultural, political and military realities in the US allies and their relations with the United States. Although extended deterrence and assurance relations have very different histories, and have to some extent been controversial through the years, there has been a rethinking of these relations in recent years. Many Europeans face a diminished threat situation as well as economic and political pressures on the maintenance of extended deterrence, and are looking at the East Asian relationships, which do not involve forward deployed forces as more attractive than NATO’s risk-and-burden-sharing concepts involving the US nuclear forces deployed in Europe. On the other hand, the East Asian allies are looking favorably at NATO nuclear consultations, and in the case of South Korea, renewed US nuclear deployments (which were ended in 1991), to meet increased security concerns posed by a nuclear North Korea and more assertive China. This paper explores the history of current relationships and the changes that have led the allies to view those of others as more suitable for meeting their current needs.     

Production lot sizing under setup and worker learning

Previous lot-sizing models incorporating learning effects focus exclusively on worker learning. We extend these models to include the presence of setup learning, which occurs when setup costs exhibit a learning curve effect as a function of the number of lots produced. The joint worker/setup learning problem can be solved to optimality by dynamic programming. Computational experience indicates, however, that solution times are sensitive to certain problem parameters, such as the planning horizon and/or the presence of a lower bound on worker learning. We define a two-phase EOQ-based heuristic for the problem when total transmission of worker learning occurs. Numerical results show that the heuristic consistently generates solutions well within 1% of optimality.     

Linking a throughput simulation to a systems dynamics simulation to assess the utility of a US Navy foreign humanitarian aid mission

The success of any humanitarian aid mission is generally measured by the timeliness of critical supplies that are delivered to the affected area. However, a more interesting analysis may be to determine the effect of the aid on the overall satisfaction of the local population. The authors' research focused on the delivery of humanitarian aid to a notional region that was decimated by flooding with ships, landing craft and security personnel provided by the US Navy and Marines. While the research effort addressed naval force structure, the focus of the research was: (1) to assess different delivery methods for the aid; and (2) to determine how the aid delivery impacted the overall satisfaction of the local population. To examine both concerns, two simulation models were developed, with one examining the throughput of aid delivered during the operation, and the other the satisfaction of the population based on the humanitarian aid effort.