The fast and cost-e cient home delivery of goods ordered online is logistically chal- lenging. Many companies are looking for new ways to cross the last-mile to their customers. One technology-enabled opportunity that recently has received much at- tention is the use of a drone to support deliveries. An innovative last-mile delivery concept in which a truck collaborates with a drone to make deliveries gives rise to a new variant of the traveling salesman problem (TSP) that we call the TSP with drone. In this paper, we model this problem as an IP and develop several fast route rst-cluster second heuristics based on local search and dynamic programming. We prove worst-case approximation ratios for the heuristics and test their performance by comparing the solutions to the optimal solutions for small instances. In addition, we apply our heuristics to several arti cial instances with di erent characteristics and sizes. Our experiments show that substantial savings are possible with this concept in comparison to truck-only delivery.

Additional Metadata
Persistent URL dx.doi.org/http://dx.doi.org/10.2139/ssrn.2639672, hdl.handle.net/1765/107401
Journal Transportation Science
Citation
Agatz, N.A.H, Bouman, P.C, & Schmidt, M.E. (2018). Optimization Approaches for the Traveling Salesman Problem with Drone. Transportation Science, Accepted. doi:10.2139/ssrn.2639672