The search theory open literature has paid little, if any, attention to the multiple‐searcher, moving‐target search problem. We develop an optimal branch‐and‐bound procedure and six heuristics for solving constrained‐path problems with multiple searchers. Our optimal procedure outperforms existing approaches when used with only a single searcher. For more than one searcher, the time needed to guarantee an optimal solution is prohibitive. Our heuristics represent a wide variety of approaches: One solves partial problems optimally, two use paths based on maximizing the expected number of detections, two are genetic algorithm implementations, and one is local search with random restarts. A heuristic based on the expected number of detections obtains solutions within 2% of the best known for each one‐, two‐, and three‐searcher test problem considered. For one‐ and two‐searcher problems, the same heuristic's solution time is less than that of other heuristics. For three‐searcher problems, a genetic algorithm implementation obtains the best‐known solution in as little as 20% of other heuristic solution times. © 1996 John Wiley & Sons, Inc.
The search theory open literature has paid little, if any, attention to the multiple-searcher, moving-target search problem. We develop an optimal branch-and-bound procedure and six heuristics for solving constrained-path problems with multiple searchers. Our optimal procedure outperforms existing approaches when used with only a single searcher. For more than one searcher, the time needed to guarantee an optimal solution is prohibitive. Our heuristics represent a wide variety of approaches: One solves partial problems optimally, two use paths based on maximizing the expected number of detections, two are genetic algorithm implementations, and one is local search with random restarts. A heuristic based on the expected number of detections obtains solutions within 2% of the best known for each one-, two-, and three-searcher test problem considered. For one-and two-searcher problems, the same heuristic's solution time is less than that of other heuristics. For threesearcher problems, a genetic algorithm implementation obtains the best-known solution in as little as 20% ofother heuristic solution times. 0 1996 John Wiley & Sons, Inc. The constrained-path, moving-target search problem [ 6 , 15, 161 has the following characteristics: 0 A single searcher and target move among a finite set of cells in discrete time. 0 The searcher and target occupy only one cell each time period. 0 Each time period, the searcher moves from its current cell to one of a specified 0 The target moves among cells according to a specified stochastic process. 0 If the target occupies the searched cell, the random search formula determines the probability of detection-otherwise the detection probability is zero. 0 The target's probability distribution is Bayesian updated for nondetection each time period. set of accessible cells.The objective of the search is to find a feasible search path that maximizes the probability of detecting the target in T time periods. The main contributions of this article center around extending the constrained-path, moving-target search problem to consider multiple searchers explicitly. As this article demonstrates, exact procedures developed to effectively solve single-searcher versions of this
This paper presents an actual application of the FITradeoff method to support strategic decisions on military budgets. Using Optimization Techniques and a particular Multicriteria Decision Aid method to assess budget allocation efficiency, a model was built for the Brazilian Defense Ministry. A case study of the Brazilian Navy is described in this paper, whereby this model was used. The focus is on the adaptations of FITradeoff necessary to make it suitable for different groups of decision-makers and staff, performing different tasks on the process. The FITradeoff proposal allowed a group decision with different levels suitable to the military context and high-level strategic decisions. The use of multicriteria in this approach provides a meaningful utility measure. This provides a score value representing the desirability of an alternative, in terms of its attainment of the institution´s strategic objectives. The approach contributed to an approximate gain of 15% in budget efficiency in 2022.
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