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2019年 美赛优秀论文 Multi-Directional Comprehensive Disaster Response System...

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    奋斗
    2020-11-29 11:37
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    发表于 2021-1-9 15:29 |显示全部楼层
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    2019年 美赛优秀论文 Multi-Directional Comprehensive Disaster Response  System...




    According to the actual situation of Puerto Rico, we designed a disaster response system
    from the perspective of disaster area demand, company cost, realizability and security.
    First, we identified the number and type of UAVs(unmanned aerial vehicle) in the UAV
    fleet based on the geographical location and needs of Puerto Rican hospitals. Minimum UAVs
    are used to save costs. Solving this optimization problem, we get two schemes: scheme one
    needs four UAVs (B, C, D and H), the number of which is 1B, 1C, 1D and 3H; scheme two
    needs four UAVs (B, C, G and H), the number of which is 2B, 1C, 1G and 3H. Each scheme
    needs three containers.
    Second, we designed the packaging configuration for containers. The number of medical
    packages is large, so the heuristic algorithm is not effective. We propose a one-dimensional
    maximum utilization packing scheme of “medical package first, UAV later”. It can not only
    realize the greater use of container space, but also be easy to achieve when loading containers.
    The maximum space utilization rate is 93.22% and the minimum utilization rate is 68.14%.
    Third, we gridded the main roads in Puerto Rico's main disaster areas and transformed
    the continuous problems into discrete ones. We identified the optimal location of the disaster
    response system by using grid search method. The three containers’ locations are as follows:
    ,
    ,
    .
    Fourth, the payload packaging configuration of UAV is designed by using optimization
    methods. Drone B load 2MED1,drone C load 1MED1+1MED3, drone D load 4MED1+2MED3
    or 3MED1+3MED2 or 2 MED1+1MED2+2MED3. UAV flight delivery routes need to avoid
    mountain and high buildings, so we use Voronio Diagram and Dijkstra algorithm to get
    delivery route. The flight schedule of UAV is obtained according to the delivery route.
    Fifth, in order to make the UAV reconnaissance the road as wide as possible, flight
    schedule of the UAV are obtained by using ant colony optimization (ACO). It can use the
    limited flight time to reconnoitre the road as much as possible.
    To sum up, we considered many factors to design DroneGo system.
    Keywords: Optimization; ACO; Gridding; Voronio; Dijkstra


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