2011 HiMCM B题特等奖学生论文下载3180

2011 HiMCM B题特等奖学生论文下载3180

 

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论文摘要如下：

Math Modeling Team #3180 Summary Sheet

In this solution, we created innovative models that can be used to solve two common problems that were presented to us: computing the probability of finding a lost item in a small park, and developing the most effective method for searching a larger park for a lost jogger. Since the two problems had numerous similarities, we had similar solutions to both of the problems. To create these solutions, we had to first identify the variables that had to be considered in our models. Many of the variables were similar for both problems, and some of them included, but were not limited to: distance of trails, points of interests in the park, weather at the time of the event, parking station locations, the condition of the jogger, and some things even as detailed as the strength of the light of the searchers. Our model took into account all of these variables, and used maps, graphs, flow charts, and equations to come up with the optimum solution to the problem. The strengths of this model are that it accounts for a multitude of variables and the use of many visuals (such as maps, graphs, and flow charts) which help to visualize and allow for a more precise solution. Moreover, the equation that we developed for part B of the problem (the lost jogger) effectively finds the probability of finding the lost jogger while taking into account a myriad of variables and also helps to find the jogger. One weakness to our overall model is that many assumptions had to be made with limited data. Some of these assumptions include the general starting point of the jogger, ruling out areas where the jogger could not possibly be, and assuming that the lost object had to be on a trail. The way that our model will be tested is through actual values being plugged in (for the case of our equation) and real life experiences in similar situations to the given problem (lost items and lost joggers).

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