A multilevel passenger screening problem for aviation security

Passenger prescreening is a critical component of aviation security systems. This paper introduces the Multilevel Allocation Problem (MAP), which models the screening of passengers and baggage in a multilevel aviation security system. A passenger is screened by one of several classes, each of which corresponds to a set of procedures using security screening devices, where passengers are differentiated by their perceived risk levels. Each class is defined in terms of its fixed cost (the overhead costs), its marginal cost (the additional cost to screen a passenger), and its security level. The objective of MAP is to assign each passenger to a class such that the total security is maximized subject to passenger assignments and budget constraints. This paper shows that MAP is NP‐hard and introduces a Greedy heuristic that obtains approximate solutions to MAP that use no more than two classes. Examples are constructed using data extracted from the Official Airline Guide. Analysis of the examples suggests that fewer security classes for passenger screening may be more effective and that using passenger risk information can lead to more effective security screening strategies. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

On the bivariate negative binomial distribution of mitchell and paulson

The bivariate negative binomial distribution of Mitchell and Paulson [17] for the case b = c = 0 is shown to be equivalent to the accident proneness model of Edwards and Gurland [4] and Subrahmaniam [19,20]. The diagonal series expansion of its joint probability function is then derived. Two other formulations of this distribution are also considered: (i) as a mixture model, which showed how it arises as the discrete analogue to the Wicksell-Kibble bivariate gamma distribution, and (ii) as a consequence of the linear birth-and-death process with immigration.     

Landmarks in defense literature

Some Principles of Maritime Strategy. Julian S. Corbett. Longmans, London, 1911     

A simple Markov model for the analysis of multiple air combat

It is proposed to describe multiple air-to-air combat having a moderate number of participants with the aid of a stochastic process based on end-game duels. A simple model describing the dominant features of air combat leads to a continuous time discrete-state Markov process. Solution of the forward Kolmogorov equations enables one to investigate the influence of initial force levels and performance parameters on the outcome probabilities of the multiple engagement. As is illustrated, such results may be useful in the decision-making process for aircraft and weapon system development planning. Some comparisons are made with Lanchester models as well as with a semi-Markov model.     

The discrete max-min problem

The historic max-min problem is examined as a discrete process rather than in its more usual continuous mode. Since the practical application of the max-min model usually involves discrete objects such as ballistic missiles, the discrete formulation of the problem seems quite appropriate. This paper uses an illegal modification to the dynamic programming process to obtain an upper bound to the max-min value. Then a second but legal application of dynamic programming to the minimization part of the problem for a fixed maximizing vector will give a lower bound to the max-min value. Concepts of optimal stopping rules may be applied to indicate when sufficiently near optimal solutions have been obtained.