Cyclic best first search: Using contours to guide branch‐and‐bound algorithms

The cyclic best‐first search (CBFS) strategy is a recent search strategy that has been successfully applied to branch‐and‐bound algorithms in a number of different settings. CBFS is a modification of best‐first search (BFS) that places search tree subproblems into contours which are collections of subproblems grouped in some way, and repeatedly cycles through all non‐empty contours, selecting one subproblem to explore from each. In this article, the theoretical properties of CBFS are analyzed for the first time. CBFS is proved to be a generalization of all other search strategies by using a contour definition that explores the same sequence of subproblems as any other search strategy. Further, a bound is proved between the number of subproblems explored by BFS and the number of children generated by CBFS, given a fixed branching strategy and set of pruning rules. Finally, a discussion of heuristic contour‐labeling functions is provided, and proof‐of‐concept computational results for mixed‐integer programming problems from the MIPLIB 2010 database are shown. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 64–82, 2017     

Satellite constellation design for forest fire monitoring via a stochastic programing approach

There is significant value in the data collected by satellites during and after a natural disaster. The current operating paradigm in practice is for satellites to passively collect data when they happen to fly over a disaster location. Conversely, this article considers the alternative approach of actively maneuvering satellites to fly directly overhead of the disaster site on a routine basis. Toward this end, we seek to compute a satellite constellation design that minimizes the expected maneuver costs for monitoring an unknown forest fire. In this article, we present a 2‐stage stochastic programing model for this problem as well as a accelerated L‐shaped decomposition approach. A comparison between our approach and the current operating paradigm indicates that our solution provides longer duration data collections and a greater number of data collections. Analysis also shows that our proposed solution is robust over a wide array of scenarios.     

The LP/POMDP marriage: Optimization with imperfect information

A new technique for solving large‐scale allocation problems with partially observable states and constrained action and observation resources is introduced. The technique uses a master linear program (LP) to determine allocations among a set of control policies, and uses partially observable Markov decision processes (POMDPs) to determine improving policies using dual prices from the master LP. An application is made to a military problem where aircraft attack targets in a sequence of stages, with information acquired in one stage being used to plan attacks in the next. © 2000 John Wiley & Sons, Inc., Naval Research Logistics 47: 607–619, 2000     

Learning to Fight and Fighting to Learn: Practitioners and the Role of Unit Publications in VIII Fighter Command 1943–1944

A military cannot hope to improve in wartime if it cannot learn. Ideally, in wartime, formal learning ceases and the application of knowledge begins. But this is optimistic. In 1942, USAAF Eighth Air Force assumed it had the means necessary for victory. In reality, its technique and technology were only potentially – rather than actually – effective. What remained was to create the practice of daylight bombing – to learn. This article (1) recovers a wartime learning process that created new knowledge, (2) tests existing tacit hypotheses in military adaptation research, and (3) offers additional theoretical foundation to explain how knowledge is created in wartime     

Interrelationships between performance measures for the machine-repairman problem

The machine-repairman problem with heterogeneous machines and multiple repairmen is considered. It is assumed that machine up times and repair times have general distributions, and a work-conserving service discipline is employed. Under these general assumptions, steady-state relationships between performance measures are examined for the individual machines. Also, various performance measures are defined for the aggregate system and interrelationships between these measures are established.     

Optimal layered search

This article investigates the problem of planning an optimal layered search for a stationary target. A search is layered if it assigns each point in the search space an integer number of glimpses or looks. General necessary and sufficient conditions and an existence theorem are obtained. The special case in which the initial target distribution is bivariate normal is considered in detail.     

Interactive multicriteria linear programming: An extension of the method of Zionts and Wallenius

This article presents an extension of the interactive multicriteria linear-programming method of Zionts and Wallenius [see Management Science, 29 (5) (1983)]. The decision maker's underlying utility function is assumed to be pseudoconcave, and his preference structure is assessed through pairwise comparison questions. In the method of Zionts and Wallenius, the decision maker's preference structure is represented as constraints on the weights on the objectives derived from his responses. This representation is only a linear approximation to the underlying nonlinear utility function. Accordingly, inconsistency among the constraints on the weights arises while solving the problem. Therefore, some of the constraints have to be dropped, resulting in a loss of information on the preference structure, and hence an increase in the total number of questions. In this article we develop a hybrid representation scheme to avoid this problem. The proposed scheme is implemented within the algorithmic framework of the method of Zionts and Wallenius, and its underlying theory is developed. Computational results show that the number of questions required by the Zionts and Wallenius method can be sinificantly reduced using the proposed scheme.     

Facility location on a grid with a diagonal line

This article extends the traditional median problem on a grid to include a diagonal line (e.g., a street). In contrast to the traditional median problem, this generalized problem is nonconvex and nonseparable, invalidating some of the properties on which well-known median-seeking algorithms are based. This work presents an algorithm for finding the median on this generalized grid and discusses the relationship between it and the traditional median.