Markovian models of three‐on‐one combat involving a hidden defender

In this paper, Markovian models of three‐on‐one stochastic firefights between ground‐based weapon systems are developed. These models address a common scenario of interest to the military, but one which has been much neglected in analytic combat models, that of combat between a hidden defender and an exposed attacking force. Each combatant must detect an opponent before commencing their firing cycle, a task which is considerably more difficult for the attacker. In the models developed here, the defender detects the exposed attacking group after an exponentially distributed time interval, while each attacker has a fixed probability of detecting the defender via the flash signature produced after each shot fired by him. The utility of the approach is demonstrated by investigating what impact the introduction of a coordinated gun‐laying system for the attacking force might have, a system made possible by battlefield digitization. The method used here allows models to be developed incrementally. This and other advantages of the Markovian approach are discussed. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 627–646, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10041     

Rare‐event component importance for the consecutive‐k system

Various indices of component importance with respect to system reliability have been proposed. The most popular one is the Birnbaum importance. In particular, a special case called uniform Birnbaum importance in which all components have the same reliability p has been widely studied for the consecutive‐k system. Since it is not easy to compare uniform Birnbaum importance, the literature has looked into the case p = ½, p → 1, or p ≥ ½. In this paper, we look into the case p → 0 to complete the spectrum of examining Birnbaum importance over the whole range of p. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 159–166, 2002; DOI 10.1002/nav.10001     

Analysis of down times of jib cranes—A stochastic approach

In this paper a case study dealing with the maintenance problem of jib cranes is presented. A jib crane is viewed as a complex system whose performance is observed as a single realization over period of time. After pointing out limitations of existing stochastic models to analyze the observed realization a new family of bivariate stochastic processes is introduced. The data of jib crane is analyzed using new model and cross‐validated using part of the data set. It is noted that the new family of stochastic processes is useful to analyze bivariate data where one of the variables is finitely valued and the other is nonnegative and continuous. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 231–243, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10006     

Resource efficiency in aircraft production

This article examines measures of economic efficiency in aircraft production. In particular, a type of nonlinear frontier estimation is contrasted with more traditional methods for estimating a dynamic cost function. This cost function is grounded in economic theory, and it is consistent with knowledge of the aircraft-production process. The model includes the effects of both learning and production rate on total program costs. The usefulness of the model is demonstrated with an example that relates to the acquisition of military equipment. It is shown through various sensitivity analyses that an alternative procurement policy for an aircraft program could have resulted in increased efficiency and hence a lower total program cost to the government.     

A nonidentical parallel processor scheduling problem

The problem considered in this article is a generalization of the familiar makespan problem, in which n jobs are allocated among m parallel processors, so as to minimize the maximum time (or cost) on any processor. Our problem is more general, in that we allow the processors to have (a) different initial costs, (b) different utilization levels before new costs are incurred, and (c) different rates of cost increase. A heuristic adapted from the bin-packing problem is shown to provide solutions which are close to optimal as the number of iterations is allowed to increase. Computational testing, over a large number of randomly generated problem instances, suggests that heuristic errors are, on average, very small.     

Determination of efficient points in multiple-objective location problems

This article is devoted to an MCDM problem connected with locational analysis. The MCDM problem can be formulated so as to minimize the distance between a facility and a given set of points. The efficient points of this problem are candidates for optimal solutions to many location problems. We propose an algorithm to find all efficient points when distance is measured by any polyhedral norm.     

A duality model for a generalized minmax program

We consider a generalized minmax programming problem, and establish, under certain weaker convexity assumptions, the Fritz John sufficient optimality conditions for such a problem. A dual program is introduced and using those optimality conditions duality theorems are proved relating the dual and the primal. Duality for the generalized fractional programming problem is considered as an application of the results proved.     

Minimizing quality control costs for laboratory instruments using information delays

This paper proposes the use of a cost-based statistical process control system for monitoring the quality of an unreliable analytical machine. In this environment the accuracy of the process cannot be determined by inspecting the output, but must be verified by analyzing standards of known values. The results demonstrate that a significant cost savings can be obtained if analytical results are placed in a “buffer” and their release delayed until the accuracy of the test can be assured. Results suggest the approach not only dramatically improves the quality of results reported but also significantly reduces the expected total cost of testing. © 1996 John Wiley & Sons, Inc.     

Algorithms for the constrained maximum-weight connected graph problem

Given a positive integer R and a weight for each vertex in a graph, the maximum-weight connected graph (MCG) problem is to find a connected subgraph with R vertices that maximizes the sum of the weights. The MCG problem is strongly NP-complete, and we study a special case of it: the constrained MCG (CMCG) problem, which is the MCG problem with a constraint of having a predetermined vertex included in the solution. We first show that the Steiner tree problem is a special case of the CMCG problem. Then we present three optimization algorithms for the CMCG problem. The first two algorithms deal with special graphs (tree and layered graphs) and employ different dynamic programming techniques, solving the CMCG problem in polynomial times. The third one deals with a general graph and uses a variant of the Balas additive method with an imbedded connectivity test and a pruning method. We also present a heuristic algorithm for the CMCG problem with a general graph and its bound analysis. We combine the two algorithms, heuristic and optimization, and present a practical solution method to the CMCG problem. Computational results are reported and future research issues are discussed. © 1996 John Wiley & Sons, Inc.