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Development of A Collaborative Delivery System With Unmanned Aerial Vehicles and Delivery Trucks

BROWSE_DETAIL_CREATION_DATE: 19-06-2018

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BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: EngD

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: TECHNOLOGY,

BROWSE_DETAIL_CREATORS: Aboharba, Salah Elhadi Khalifa (Author),

BROWSE_DETAIL_CONTRIBUTERS: Arıkan, Kutluk Bilge (Advisor),

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UAV; delivery optimization routing problem; integer linear programming; K-means cluster algorthm; nearest neighbor algorithm; ant colony optimization algorithm.


BROWSE_DETAIL_TAB_ABSTRACT

This thesis studies the new application for an unmanned aerial vehicle in the delivery system. Considering a problem of the limited flight time of UAV due to the small battery package that challenges the distribution of the goods directly from the main warehouse difficult, therefore, a collaborative delivery system with UAVs and delivery trucks is proposed. This research focuses on the optimization of the routing problems where a delivery truck is utilized as the base for the UAV when it performs a delivery task. First, the mathematical formulation is developed, with two stages, namely the UAV power consumption model and integer linear programming model, followed by the problem being solved with the K-means algorithm (to partition customers into groups and find the best location for the delivery truck) and with an ant colony optimization algorithm and nearest neighbor algorithm to tackle the routing problem for the UAV for each group. All the algorithms are implemented in MATLAB to find the location of the delivery truck, to minimize the distance traveled and minimize delivery time taking into account the power consumption of UAVs. Finally, comparisons between this system and truck usage is presented. The results show that the delivery time in the collaborative delivery system is reduced compared with truck only usage. Moreover, the issue of limited flight time is solved by applying this system. In addition, a method is developed to weight between thevhighest demand and shortest distance for the UAV to select a path at minimum power consumption when the demand of the customers is not equal. This method is enforced in nearest neighbor algorithm and ant colony optimization algorithm and the results show that nearest neighbor algorithm is more efficient then ant colony optimization algorithm.


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