Integrated scheduling of m-truck, m-drone, and m-depot constrained by time-window, drop-pickup, and m-visit using constraint programming

• Published in: Transportation research. Part C, Emerging technologies Vol. 91; pp. 1 - 14
• Main Author: Ham, Andy M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2018
• Subjects: Constraint programming; Traveling salesman problem; Unmanned aerial vehicle; Vehicle routing problem; Traveling salesman problem; Unmanned aerial vehicle; Vehicle routing problem; Constraint programming
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2018.03.025

•A drone scheduling problem is extended to consider drop and pickup syncronization.•A constraint programming is applied to UAVs transportation scheduling problem.•Integrated scheduling of multiple depots is studied. The idea of deploying unmanned aerial vehicles, also known as drones, for final-mile delivery in logistics operations has vitalized this new research stream. One conceivable scenario of using a drone in conjunction with a traditional delivery truck to distribute parcels is discussed in earlier literature and termed the parallel drone scheduling traveling salesman problem (PDSTSP). This study extends the problem by considering two different types of drone tasks: drop and pickup. After a drone completes a drop, the drone can either fly back to depot to deliver the next parcels or fly directly to another customer for pickup. Integrated scheduling of multiple depots hosting a fleet of trucks and a fleet of drones is further studied to achieve an operational excellence. A vehicle that travels near the boundary of the coverage area might be more effective to serve customers that belong to the neighboring depot. This problem is uniquely modeled as an unrelated parallel machine scheduling with sequence dependent setup, precedence-relationship, and reentrant, which gives us a framework to effectively consider those operational challenges. A constraint programming approach is proposed and tested with problem instances of m-truck, m-drone, m-depot, and hundred-customer distributed across an 8-mile square region.