Optimal Search Plan for a Stationary Target Minimizing the Expected Risk

Author(s):  
Kōji Iida
Author(s):  
Luyao Zhu ◽  
Wenxi Hao ◽  
Zhiwei Zhu
Keyword(s):  

2016 ◽  
Vol 13 (06) ◽  
pp. 1650038 ◽  
Author(s):  
Mohamed Abd Allah El-Hadidy

This paper presents a search model that finds [Formula: see text]-dimensional randomly moving target in which any information of the target position is not available to the searchers. There exist [Formula: see text]-searchers, starting the searching process from the origin. Rather than finding the conditions that make the expected value of the first meeting time between one of the searchers and the target is finite, this work shows the existence of the fuzzy optimal search plan that minimizes the expected value of the first meeting time. The effectiveness of this method is illustrated by using an example with numerical results.


2020 ◽  
Vol 8 (2) ◽  
pp. 471-480
Author(s):  
Min-hyuk Kim ◽  
Suhwan Kim ◽  
Bongkyu Han

This article deals with a one-searcher multi-target search problem where targetswith different detection priorities move in Markov processes in each discrete time interval over agiven space search area, and the total number of search time intervals is fixed. A limitedsearch resource is available in each search time interval and an exponential detection functionis assumed. The searcher can obtain a target detection reward, if the target is detected, whichrepresents the detection priority of target and does not increase with respect to time. The objective is toestablish the optimal search plan that allocates the search resource effort over the search areasin each time interval in order to maximize the total detection reward. The analysis shows that the given problem can be decomposed into interval-wise individualsearch problems, each being treated as a single stationary target problem for each timeinterval. Thus, an iterative procedure is derived to solve a sequence of stationary targetproblems. The computational results show that the proposed algorithm guaranteesoptimality.


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