Capacitated Vehicle Routing and Assignment Problem In A Drone Based Environment Using Google Operations Research Tools

Unmanned aerial vehicles, or drones, have the potential to significantly reduce the cost and time of making last-mile deliveries and responding to emergencies. Despite this potential, little work has gone into developing vehicle routing problems (VRPs) specifically for drone delivery scenarios. Existing VRPs are insufficient for planning drone deliveries: either multiple trips to the depot are not permitted, leading to solutions with excess drones, or the effect of battery and payload weight on energy consumption is not considered, leading to costly or infeasible routes. This project focuses on building an optimization model, given a fleet of drones, a list of customer orders, and the availability of the individual products in warehouses. Warehouses are assigned to orders in a way that minimizes the total distance traveled by drones to fulfill orders. This model can be applicable to almost all retail stores which have their clientele in proximity to their store like Rite Aid, Giant, Walmart, etc... By considering the parameters such as the location of the warehouse, location of the customers, the weight of the products which are being delivered, carrying capacity of the drone, and the battery constraint of the drone we can make a model which can be further tweaked and customized to cater to situations where the parameters are optimized. The goal of this paper is to introduce a solid framework for a drone-based network optimization problem to aid in the implementation of drones into the commercial system. This paper starts with an introduction to the literature survey of the previous work in this field of network optimization and assignment problems, then we move forward to how the model was combined formulated, and implemented for our specialized study where drones are used to deliver the products.

Guide: Dr. Vittal V. Prabhu

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Work Title Capacitated Vehicle Routing and Assignment Problem In A Drone Based Environment Using Google Operations Research Tools
Access
Open Access
Creators
  1. Suhas Hurli Gopalakrishna
Keyword
  1. Capacitated Vehicle Routing
  2. Optimization, Drone-based delivery, Scheduling
  3. Drone-based delivery
  4. Scheduling
  5. Supply Chain Optmization
License Public Domain Mark 1.0
Work Type Research Paper
Acknowledgments
  1. Dr. Vittal V. Prabhu
  2. Dr. Paul Griffin
Publication Date 2021
Subject
  1. Supply Chain
Language
  1. English
DOI doi:10.26207/rxff-jp89
Source
  1. Google Repository
Deposited March 22, 2021

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    Description
    • Unmanned aerial vehicles, or drones, have the potential to significantly reduce the cost and time of making last-mile deliveries and responding to emergencies. Despite this potential, little work has gone into developing vehicle routing problems (VRPs) specifically for drone delivery scenarios. Existing VRPs are insufficient for planning drone deliveries: either multiple trips to the depot are not permitted, leading to solutions with excess drones, or the effect of battery and payload weight on energy consumption is not considered, leading to costly or infeasible routes. This project focuses on building an optimization model, given a fleet of drones, a list of customer orders, and the availability of the individual products in warehouses. Warehouses are assigned to orders in a way that minimizes the total distance traveled by drones to fulfill orders. This model can be applicable to almost all retail stores which have their clientele in proximity to their store like Rite Aid, Giant, Walmart, etc... By considering the parameters such as the location of the warehouse, location of the customers, the weight of the products which are being delivered, carrying capacity of the drone, and the battery constraint of the drone we can make a model which can be further tweaked and customized to cater to situations where the parameters are optimized. The goal of this paper is to introduce a solid framework for a drone-based network optimization problem to aid in the implementation of drones into the commercial system. This paper starts with an introduction to the literature survey of the previous work in this field of network optimization and assignment problems, then we move forward to how the model was combined formulated, and implemented for our specialized study where drones are used to deliver the products.
    • Unmanned aerial vehicles, or drones, have the potential to significantly reduce the cost and time of making last-mile deliveries and responding to emergencies. Despite this potential, little work has gone into developing vehicle routing problems (VRPs) specifically for drone delivery scenarios. Existing VRPs are insufficient for planning drone deliveries: either multiple trips to the depot are not permitted, leading to solutions with excess drones, or the effect of battery and payload weight on energy consumption is not considered, leading to costly or infeasible routes. This project focuses on building an optimization model, given a fleet of drones, a list of customer orders, and the availability of the individual products in warehouses. Warehouses are assigned to orders in a way that minimizes the total distance traveled by drones to fulfill orders. This model can be applicable to almost all retail stores which have their clientele in proximity to their store like Rite Aid, Giant, Walmart, etc... By considering the parameters such as the location of the warehouse, location of the customers, the weight of the products which are being delivered, carrying capacity of the drone, and the battery constraint of the drone we can make a model which can be further tweaked and customized to cater to situations where the parameters are optimized. The goal of this paper is to introduce a solid framework for a drone-based network optimization problem to aid in the implementation of drones into the commercial system. This paper starts with an introduction to the literature survey of the previous work in this field of network optimization and assignment problems, then we move forward to how the model was combined formulated, and implemented for our specialized study where drones are used to deliver the products.
    • Guide: Dr. Vittal V. Prabhu
  • Updated Acknowledgments Show Changes
    Acknowledgments
    • Dr. Vittal V. Prabhu, Dr. Paul Griffin
  • Added Creator Suhas Hurli Gopalakrishna
  • Added Suhas Hurli Gopalakrishna Drone delivery optimization.pdf
  • Updated License Show Changes
    License
    • http://creativecommons.org/publicdomain/mark/1.0/
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