Study Release
The study is available upon request.Material on this web site can be used freely
in any publication provided that:
- It is duly credited as a project by Mayar Ariss
- A PDF copy of the publication is sent to eddy.ariss19@imperial.ac.uk
The global increase in urban population directly affects the efficiency of existing transportation systems. With technological progress, alternatives to traditional transportation methods are explored. A drone mail delivery system is here proposed to reduce delays in transportation networks.
This project aims to optimise a network of hyperlocal last-mile distribution depots in London, in the districts of Camden, City of London, Westminster, Kensington and Chelsea. The drones dispatch mail directly to customers, aiming at delivery times of 7 minutes or less from their original departure warehouse. A facility selection strategy is also outlined, which allows further expansion to take place.
Global urbanization has led to one of the world’s most pressing environmental health concerns: the
increasing number of people contributing to and being affected by air pollution, leading to 7
million early deaths each year. The key issue is human exposure to pollution within cities and the
consequential effects on human health.
Mail Drones is particularly relevant in the present context of global warming, and would also
contribute to increase the air quality in cities and mitigate human exposure to dangerous air
pollution.
The main objective is to address the outlined urban issues, while ensuring that the facility expansion strategy maximises profit. The mathematical model takes as inputs the expected number of deliveries per month in a given neighbourhood, which depends on its total population and the time since the beginning of regional operations.
Economic constraints are set considering the total initial budget, depot rent, operating costs, and average revenue of which a certain percentage is set aside for further expansion. New warehouses are established every two months, providing enough funds are available to cover their cost of operation.
In this case study, the data is publicly available at Greater London
Authority. With Python, the geospatial data is generated using GeoPandas. Then, the locations
of the optimal combination of depots is determined using PulP.
Economic constraints are set considering the total initial budget, depot rent, operating costs, and
average revenue of which a certain percentage is set aside for further expansion. New warehouses are
established every two months, providing enough funds are available to cover their cost of operation.
