An indicator of combat success

Assessing the effects of changes in weapons systems or battle tactics is difficult because of the variations in battles and the resulting instability of measures of combat effectiveness. Even under the relatively stable conditions of designed combat exercises, traditional measures may fail to reflect important battle events and dynamics. This variation in results makes the design, conduct, and evaluation of combat experiments, combat simulations, and combat training exercises a challenging endeavor, indeed. We develop and examine a combat measure of effectiveness, based on Lanchester models, which we call the battle trace. The battle trace is a measure of ongoing battle results, measured as a function of time into the battle. We describe how such measures might contribute to assessing effects of changing certain battle parameters. We suggest applications of these ideas to developing combat simulations and to enhancing combat training exercises.     

Static and dynamic scheduling of customer arrivals to a single-server system

In this article we consider a single-server system whose customers arrive by appointments only. Both static and dynamic scheduling problems are studied. In static scheduling problems, one considers scheduling a finite number of customer arrivals, assuming there is no scheduled customer arrival to the system. In dynamic scheduling problems, one considers scheduling one customer arrival only, assuming that there are a number of scheduled customers already. The expected delay time is recursively computed in terms of customer interarrival times for both cases. The objective is to minimize the weighted customer delay time and the server completion time. The problem is formulated as a set of nonlinear equations. Various numerical examples are illustrated. © 1993 John Wiley & Sons, Inc.     

Location-allocation for distribution to a uniform demand with transshipments

This article analyzes the location-allocation problem for distribution from a single fixed origin via transshipment terminals to a continuous uniformly distributed demand. Distribution through terminals concentrates flows on the origin-to-terminal links and transportation economies of scale encourage the use of larger vehicles. Analytical expressions are derived for the optimal terminal locations, the optimal allocation of destinations to terminals, and the optimal transportation cost. Continuous analytic models assume either an allocation, by partitioning the service region into sectors, or terminal locations. This is unlikely to produce an optimal distribution system. The optimal cost is compared to the cost for suboptimal location-allocation combinations. Results indicate that the location decision is not too important if destinations are allocated optimally and that allocation to the nearest terminal may be poor, even with optimal locations. © 1992 John Wiley & Sons, Inc.     

Simulation of stochastic activity networks using path control variates

This article details several procedures for using path control variates to improve the accuracy of simulation-based point and confidence-interval estimators of the mean completion time of a stochastic activity network (SAN). Because each path control variate is the duration of the corresponding directed path in the network from the source to the sink, the vector of selected path controls has both a known mean and a known covariance matrix. This information is incorporated into estimation procedures for both normal and nonnormal responses. To evaluate the performance of these procedures experimentally, we examine the bias, variance, and mean square error of the controlled point estimators as well as the average half-length and coverage probability of the corresponding confidence-interval estimators for a set of SANs in which the following characteristics are systematically varied: (a) the size of the network (number of nodes and arcs); (b) the topology of the network; (c) the percentage of activities with exponentially distributed durations; and (d) the relative dominance of the critical path. The experimental results show that although large improvements in accuracy can be achieved with some of these procedures, the confidence-interval estimators for normal responses may suffer serious loss of coverage probability in some applications.     

Cumulative search-evasion games

Cumulative search-evasion games (CSEGs) are two-person zero-sum search-evasion games where play proceeds throughout some specified period without interim feedback to either of the two players. Each player moves according to a preselected plan. If (X_t, Y_t,) are the positions of the two players at time t, then the game's payoff is the sum over t from 1 to T of A(X_t, Y_t, t). Additionally, all paths must be “connected.” That is, the finite set of positions available for a player in any time period depends on the position selected by that player in the previous time period. One player attempts to select a mixed strategy over the feasible T-time period paths to maximize the expected payoff. The other minimizes. Two solution procedures are given. One uses the Brown-Robinson method of fictitious play and the other linear programming. An example problem is solved using both procedures.     

Optimal service rates of a service facility with perishable inventory items

In this paper we optimally control service rates for an inventory system of service facilities with perishable products. We consider a finite capacity system where arrivals are Poisson‐distributed, lifetime of items have exponential distribution, and replenishment is instantaneous. We determine the service rates to be employed at each instant of time so that the long‐run expected cost rate is minimized for fixed maximum inventory level and capacity. The problem is modelled as a semi‐Markov decision problem. We establish the existence of a stationary optimal policy and we solve it by employing linear programming. Several numerical examples which provide insight to the behavior of the system are presented. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 464–482, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10021     

Estimating the probability that a simulated system will be the best

Consider a stochastic simulation experiment consisting of v independent vector replications consisting of an observation from each of k independent systems. Typical system comparisons are based on mean (long‐run) performance. However, the probability that a system will actually be the best is sometimes more relevant, and can provide a very different perspective than the systems' means. Empirically, we select one system as the best performer (i.e., it wins) on each replication. Each system has an unknown constant probability of winning on any replication and the numbers of wins for the individual systems follow a multinomial distribution. Procedures exist for selecting the system with the largest probability of being the best. This paper addresses the companion problem of estimating the probability that each system will be the best. The maximum likelihood estimators (MLEs) of the multinomial cell probabilities for a set of v vector replications across k systems are well known. We use these same v vector replications to form v^k unique vectors (termed pseudo‐replications) that contain one observation from each system and develop estimators based on AVC (All Vector Comparisons). In other words, we compare every observation from each system with every combination of observations from the remaining systems and note the best performer in each pseudo‐replication. AVC provides lower variance estimators of the probability that each system will be the best than the MLEs. We also derive confidence intervals for the AVC point estimators, present a portion of an extensive empirical evaluation and provide a realistic example. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 341–358, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10019     

The rise of targeted killing

Targeted killings have become a central tactic in the United States' campaigns against militant and terrorist groups in the Middle East, Asia, and Africa. Both ‘demand’ and ‘supply’ factors explain the rise of targeted killings. Demand for targeted killings increased as the United States faced new threats from militant groups that could not be effectively countered with conventional military force. Concerns about the political consequences of long-term military involvement overseas and American casualties led political leaders to supply more targeted killings. The conclusion discusses how this tactic may have unintended consequences as other states follow the United States use of targeted killings.     

Investment in production resource flexibility: An empirical investigation of methods for planning under uncertainty

We examine several methods for evaluating resource acquisition decisions under uncertainty. Traditional methods may underestimate equipment benefit when part of this benefit comes from decision flexibility. We develop a new, practical method for resource planning under uncertainty, and show that this approach is more accurate than several commonly used methods. We successfully applied our approach to an investment problem faced by a major firm in the aviation information industry. Our recommendations were accepted and resulted in estimated annual savings in excess of $1 million (US). © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003