Abstract
In general, the demand for delivery cannot be fulfilled efficiently due to the excessive traffic in dense urban areas. Therefore, many innovative concepts for intelligent transportation of freight have recently been developed. One of these concepts relies on drone-based parcel delivery using rooftops of city buildings. To apply drone logistics system in cities, the operation design should be adequately prepared. In this regard, a mixed integer programming model for drone operation planning and a heuristic based on block stacking are newly proposed to provide solutions. Additionally, numerical experiments with three different problem sizes are conducted to check the feasibility of the proposed model and to assess the performance of the proposed heuristic. The experimental results show that the proposed model seems to be viable and that the developed heuristic provides very good operation plans in terms of the optimality gap and the computation time.
Highlights
Cities all over the world are leveraging state-of-the-art digital technologies to build open and intelligent control systems for their smart city initiatives using real-time information for public traffic, emergency management, and public security
All demands are known in advance, and the drone is launched from the vehicle, which follows a traveling salesman problem (TSP) route
Considering the above assumptions and the current drone technologies, we propose a new drone operation planning model for effectively delivering parcels in urban areas
Summary
Cities all over the world are leveraging state-of-the-art digital technologies to build open and intelligent control systems for their smart city initiatives using real-time information for public traffic, emergency management, and public security. We first propose a mixed-integer linear programming (MILP) model for generating a schedule for drone-based parcel delivery using the rooftops of buildings in cities. Any commercial optimization software can generate solutions using a sophisticated algorithm for smalland medium-sized MILP problems, it is very difficult to provide solutions for large problems that have many decision variables and constraints in a reasonable amount of time [11].
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