Approximations and empirics for stochastic war equations

The article develops a theorem which shows that the Lanchester linear war equations are not in general equal to the Kolmogorov linear war equations. The latter are time‐consuming to solve, and speed is important when a large number of simulations must be run to examine a large parameter space. Run times are provided, where time is a scarce factor in warfare. Four time efficient approximations are presented in the form of ordinary differential equations for the expected sizes and variances of each group, and the covariance, accounting for reinforcement and withdrawal of forces. The approximations are compared with “exact” Monte Carlo simulations and empirics from the WWII Ardennes campaign. The band spanned out by plus versus minus the incremented standard deviations captures some of the scatter in the empirics, but not all. With stochastically varying combat effectiveness coefficients, a substantial part of the scatter in the empirics is contained. The model is used to forecast possible futures. The implications of increasing the combat effectiveness coefficient governing the size of the Allied force, and injecting reinforcement to the German force during the Campaign, are evaluated, with variance assessments. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Search and the introduction of improved technologies

Modeling R&D as standard sequential search, we consider a monopolist who can implement a sequence of technological discoveries during the technology search process: he earns revenue on his installed technology while he engages in R&D to find improved technology. What is not standard is that he has a finite number of opportunities to introduce improved technology. We show that his optimal policy is characterized by thresholds ξ_i(x): introduce the newly found technology if and only if it exceeds ξ_i(x) when x is the state of the currently installed technology and i is the number of remaining introductions allowed. We also analyze a nonstationary learning‐by‐doing model in which the monopolist's experience in implementing new technologies imparts increased capability in generating new technologies. Because this nonstationary model is not in the class of monotone stopping problems, a number of surprising results hold and several seemingly obvious properties of the stationary model no longer hold. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Homes for heroes? Assessing the impact of the UK’s Military Covenant

The British Military Covenant can be located in and from many sources and from 2011 onwards in primary legislation. This article argues that the provision of military housing amounts to an early test of how the military covenant is understood and used by those involved in defence policy, and those in the armed forces affected by it. It finds that housing was a prominent feature of how service personnel understood how they were valued, but was not explicitly understood as a covenant issue by those personnel or the officials in charge of the Defence Estates. We locate three reasons for this: (1) the covenant has been poorly translated from aspiration into policy practice, (2) the covenant is unevenly understood across its stakeholders which has the effect of generating disappointment through misaligned expectations, (3) those engaged in the reform process surrounding the Defence Infrastructure Organisation (DIO) saw the covenant as a means to energise reform. Ultimately housing was seen as a dry and technocratic business area and thus an issue ripe for being refracted through the covenant was ultimately left outside of its remit.     

Risk pooling in a two-period,two-echelon inventory stocking and allocation problem

The object of this article is to investigate the risk-pooling effect of depot stock in two-echelon distribution system in which the depot serves n retailers in parallel, and to develop computationally tractable optimization procedures for such systems. The depot manager has complete information about stock levels and there are two opportunities to allocate stock to the retailers within each order cycle. We identify first- and second-order aspects to the risk-pooling effect. In particular, the second-order effect is the property that the minimum stock available to any retailer after the second allocation converges in probability to a constant as the number of retailers in the system increases, assuming independence of the demands. This property is exploited in the development of efficient procedures to determine near-optimal values of the policy parameters.     

On the equivalence of jit and mrp as technologies for reducing wastes in manufacturing

JIT (just-in-time) is widely regarded as an excellent tool for reducing costs and cycle times, and for improving quality in manufacturing operations. JIT follows a multistep procedure. First, it identifies and prioritizes wastes or non-value-adding activities. Second, it forces these wastes to be removed. MRP (materials requirements planning) can identify the same wastes and prioritize them in the same way that JIT does, by using data from the MRP database and master production schedule, and a waste identification model. In this article, a model is developed which describes the process by which the classic JIT system identifies and prioritizes waste. An equivalent MRP waste identification model is then developed for the production environment of the classic JIT system. (The classic JIT system was developed to produce many products having low to medium volumes.) The results developed here can be extended to other production environments where adaptations of the classic JIT system are used. An example, taken from an actual application, is presented to illustrate the models and the equivalence of JIT and MRP as systems for identifying and prioritizing wastes in manufacturing. © 1993 John Wiley & Sons, Inc.     

Vector processing approach to constrained network problems

Modification of algorithms designed for scalar computing, to take advantage of vector processing, raises several challenges. This article presents the vectorization of the primal simplex based network algorithm and results in a 50% improvement in computational time. One of the major contributors to this improvement is the matching of the size of the pricing candidate list to the vector register size. The side constraints are relaxed into a single surrogate constraint. The single constraint network algorithm is vectorized and used as the basis for solving large-scale constrained network problems. Computational experiments are presented which illustrate the vectorization of the network code as well as the ability of the surrogate constraint approach to deal with large constrained network problems.     

An infiltration game on k arcs

An infiltrator, starting at a safe base, tries to pass, undetected by a guard and within a time limit, along one of k nonintersecting arcs to a safe destination. Optimal strategies and the value are obtained for this discrete zero-sum search-evasion game.