DESIGNING A VIABLE PREDICTION MARKET TO FORECAST DEFENSE ACQUISITION COST AND SCHEDULE OUTCOMES

This paper is the first to demonstrate a viable prediction market that successfully forecasts defense acquisition cost and schedule outcomes, and to provide insights for defense executive decision‐making. Already used in private industry, prediction markets can also accurately forecast outcomes and their associated risks for government programs. Using virtual money, prediction markets allow traders to ‘bet’ on some future outcome. This market mechanism turns out to be a relatively simple and accurate way to discover, aggregate, and communicate to a defense executive the collective market’s beliefs about the likelihood of an eventual outcome of an acquisition program of interest.     

Approximation algorithms for sequential batch‐testing of series systems

We introduce and study a generalization of the classic sequential testing problem, asking to identify the correct state of a given series system that consists of independent stochastic components. In this setting, costly tests are required to examine the state of individual components, which are sequentially tested until the correct system state can be uniquely identified. The goal is to propose a policy that minimizes the expected testing cost, given a‐priori probabilistic information on the stochastic nature of each individual component. Unlike the classic setting, where variables are tested one after the other, we allow multiple tests to be conducted simultaneously, at the expense of incurring an additional set‐up cost. The main contribution of this article consists in showing that the batch testing problem can be approximated in polynomial time within factor , for any fixed . In addition, we explain how, in spite of its highly nonlinear objective function, the batch testing problem can be formulated as an approximate integer program of polynomial size, while blowing up its expected cost by a factor of at most . Finally, we conduct extensive computational experiments, to demonstrate the practical effectiveness of these algorithms as well as to evaluate their limitations. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 275–286, 2016     

A note on a model for evasive vehicle movement along a fixed path

A model for a vehicle moving evasively along a fixed path is defined in terms of a two- state semi-Markov process. An important feature of this model is the continuous movement of the vehicle as a function of time. One potential application of this model is the development of a strategy for the deployment of long-range missiles on long underground tracks.     

Maintenance scheduling for modular systems: Modeling and algorithms

We study new models of scheduled maintenance management for modular systems, consisting of multiple components with respective cycle limits. The cycle limit of each component specifies the time interval in which this component must be repaired or replaced. The goal is to compute a feasible maintenance schedule that minimizes the cost associated with component maintenance. Applications of these models arise in Air Force aircraft maintenance as well as in other arenas with required preventive maintenance. The typical cost structures that arise in practical settings are submodular, which make the resulting models computationally challenging. We develop two efficient and operationally tenable approximation algorithms. We prove constant factor worst‐case guarantees for both algorithms, and present computational experiments showing that these algorithms perform within a few percent of optimality on operationally relevant instances. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 472–488, 2014