A multi-vehicle covering tour problem with speed optimization
Preprint
Abstract
The multi-vehicle covering tour problem with time windows (MCTPTW) aims to construct a set of maximal coverage routes for a fleet of vehicles that serve (observe) a secondary set of sites given a fixed time schedule, coverage requirements, and energy restrictions. The problem is formulated as a mixed-integer second-order cone programming (MISOCP) model and is an extension of both the multi-covering tour problem and the vehicle routing problem with time windows under energy constraints. Further, we study a special case of the proposed model and develop a labeling algorithm to solve its Lagrangian relaxation problem, which exploits the combinatorial structure exhibited by an optimal solution to the Lagrangian relaxation.Keywords
All Related Versions
This publication has 37 references indexed in Scilit:
- Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A surveyNetworks, 2018
- Unmanned aerial vehicle routing in the presence of threatsComputers & Industrial Engineering, 2018
- The vehicle routing problem with drones: Extended models and connectionsNetworks, 2017
- Unmanned surface vehicles: An overview of developments and challengesAnnual Reviews in Control, 2016
- Target following with motion prediction for unmanned surface vehicle operating in cluttered environmentsAutonomous Robots, 2013
- On the optimal detection of an underwater intruder in a channel using unmanned underwater vehiclesNaval Research Logistics (NRL), 2011
- Cooperative use of unmanned sea surface and micro aerial vehicles at Hurricane WilmaJournal of Field Robotics, 2008
- An exact algorithm for the elementary shortest path problem with resource constraints: Application to some vehicle routing problemsNetworks, 2004
- Imaging from an unmanned aerial vehicle: agricultural surveillance and decision supportComputers and Electronics in Agriculture, 2004
- A new branch-and-cut algorithm for the capacitated vehicle routing problemMathematical Programming, 2004