| dc.description.abstract | Over the past few years, the recent advancements in unmanned aerial vehicles technology,
known as drones, has recently risen in the emergence of e-commerce in retail store. With
an effort to provide high responsiveness, faster and more cost-efficient delivery for goods
ordered online, companies are looking for new innovation technologies to across last-mile
to their customers. The earlier papers research the collaborative of a fleet of trucks and a
fleet of drones to adapt the demand in industry. The new variant of the Traveling
Salesman Problem, called TSP with Drone (TSP-D), aims to minimize the times at which
truck and drone finish the service. This study extends the problem by considering two
different types of drone tasks: drop and pickup. After a drone complete a drop, the drone
can fly directly to next customer to pick up the returned parcel. Two approaches are
proposed to solve the problem. The first algorithm is k-Means Clustering converts
hundreds of customers into cluster, or known as initial TSP solution. Then the optimal
TSP solution is converted to a feasible TSP-D by local search. The second algorithm, a
Greedy Randomized Adaptive Search Procedure (GRASP), generates TSP-D solution
from TSP solution and continuously improve the solution through local search. The
experiments show this concept are possible applied comparison to traditional truck
delivery. | en_US |
