A Bayesian approach to demand estimation and inventory provisioning

This article addresses the problem of explicitly taking into account uncertainty about the demand for spare parts in making inventory procurement and stockage decisions. The model described provides for a unified treatment of the closely related problems of statistical estimation of demand and resource allocation within the inventory system, and leads to an easily implemented, efficient method of determining requirements for spare parts both in the early provisioning phase and in later periods of operations when demand data have accumulated Analyses of the model's theoretical foundations and of sample outcomes of the model based upon data on parts intended for use in the F-14 lead to conclusions of great importance to both support planners and operations planners Finally, of particular significance is the ability afforded the planner by this model to quantify the impact on inventory system costs of varying levels of system reliability or management uncertainty as to projected system performance. This will provide an economic basis for analysis of such alternatives as early deployment, operational testing, and equipment redesign.     

Landmarks in defense literature

The Strategy of Conflict. By Thomas Schelling. Harvard University Press, Cambridge, MA (1960)     

Scheduling combat logistics force replenishments at sea for the US Navy

The Replenishment at Sea Planner (RASP) is saving the U.S. Navy millions of dollars a year by reducing fuel consumption of its Combat Logistics Force (CLF). CLF shuttle supply ships deploy from ports to rendezvous with underway U.S. combatants and those of coalition partners. The overwhelming commodity transferred is fuel, ship‐to‐ship by hoses, while other important packaged goods and spare parts are high‐lined, or helicoptered between ships. The U.S. Navy is organized in large areas of responsibility called numbered fleets, and within each of these a scheduler must promulgate a daily forecast of CLF shuttle operations. The operational planning horizon extends out several weeks, or as far into the future as we can forecast demand. We solve RASP with integer linear optimization and a purpose‐built heuristic. RASP plans Replenishment‐at‐Sea (RAS) events with 4‐hour (Navy watch) time fidelity. For five years, RASP has served two purposes: (1) it helps schedulers generate a daily schedule and animates it using Google Earth, and (2) it automates reports command‐to‐ship messages that are essential to keep this complex logistics system operating.     

Optimizing submarine berthing with a persistence incentive

Submarine berthing plans reserve mooring locations for inbound U.S. Navy nuclear submarines prior to their port entrance. Once in port, submarines may be shifted to different berthing locations to allow them to better receive services they require or to make way for other shifted vessels. However, submarine berth shifting is expensive, labor intensive, and potentially hazardous. This article presents an optimization model for submarine berth planning and demonstrates it with Naval Submarine Base, San Diego. After a berthing plan has been approved and published, changed requests for services, delays, and early arrival of inbound submarines are routine events, requiring frequent revisions. To encourage trust in the planning process, the effect on the solution of revisions in the input is kept small by incorporating a persistence incentive in the optimization model. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 301–318, 1997.     

Minimax resource allocation problems with ordering constraints

Resource allocation problems consider the allocation of limited resources among numerous competing activities. We address an allocation problem with multiple knapsack resource constraints. The activities are grouped into disjoint sets. Ordering constraints are imposed on the activities within each set, so that the level of one activity cannot exceed the level of another activity in the same set. The objective function is of the minimax type and each performance function is a nonlinear, strictly decreasing and continuous function of a single variable. Applications for such resource allocation problems are found, for example, in high-tech industries confronted with large-scale and complex production planning problems. We present two algorithms to solve the allocation problem with ordering constraints. The first one uses characterization of the optimal decision variables to apply a search method. The second algorithm solves a sequence of problems, each in the format of the original problem without ordering constraints. Whereas the computational effort of the first algorithm depends on the desired degree of accuracy even for linear performance functions, the effort of the latter algorithm is polynomial for certain classes of performance functions. © 1994 John Wiley & Sons, Inc.     

An examination of the effects of the criterion functional on optimal fire-support policies

This paper examines the dependence of the structure of optimal time-sequential fire-support policies on the quantification of military objectives by considering four specific problems, each corresponding to a different quantification of objectives (i.e. criterion functional). We consider the optimal time-sequential allocation of supporting fires during the “approach to contact” of friendly infantry against enemy defensive positions. The combat dynamics are modelled by deterministic Lanchester-type equations of warfare, and the optimal fire-support policy for each one-sided combat optimization problem is developed via optimal control theory. The problems are all nonconvex, and local optima are a particular difficulty in one of them. For the same combat dynamics, the splitting of supporting fires between two enemy forces in any optimal policy (i.e. the optimality of singular subarcs) is shown to depend only on whether the terminal payoff reflects the objective of attaining an “overall” military advantage or a “local” one. Additionally, switching times for changes in the ranking of target priorities are shown to be different (sometimes significantly) when the decision criterion is the difference and the ratio of the military worths (computed according to linear utilities) of total infantry survivors and also the difference and the ratio of the military worths (computed according to linear utilities) of total infantry survivors and also the difference and the ratio of the military worths of the combatants' total infantry losses. Thus, the optimal fire-support policy for this attack scenario is shown to be significantly influenced by the quantification of military objectives.