(2019) Patel, Ruchir; Herrmann, Jeffrey W; Azarm, Shapour; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Multi-vehicle route planning is the problem of determining routes for a set of vehicles
to visit a set of locations of interest. In this thesis, we describe a study of a classical
multi-vehicle route planning problem which compared existing solutions methods on
min-sum (minimizing total distance traveled) and min-max (minimizing maximum
distance traveled) cost objectives. We then extended the work in this study by
adapting approaches tested to generate robust solutions to a failure-robust multi
vehicle route planning problem in which a potential vehicle failure may require
modifying the solution, which could increase costs. Additionally, we considered a
decentralized extension to the multi-vehicle route planning problem, also known as
the decentralized task allocation problem. The results of a computational study show
that our novel genetic algorithm generated better solutions than existing approaches
on larger instances with high communication quality.
