There is a growing market for crowd-shipping, which hires people to transport packages on their regular commutes, in the Greater Toronto Area (GTA).

This thesis considers a hybrid crowd-shipping operation that hires crowd-shippers and regular drivers in a physical internet environment. An investigation into the spatial distribution of potential crowd-shippers is conducted by leveraging a crowd-shipper behaviour model and data that combines 2016 Transportation Tomorrow Survey (TTS) data and geographically aggregated census data. This thesis designs an interconnected crowd logistics network to service business-to-consumer (B2C) logistics demand through solving a parcel locker location routing problem (PLLRP) that optimizes the placement of parcel lockers and the movements of parcels. Finally, the service levels of the proposed locker locations are estimated.

The numerical results find that an interconnected crowd logistics network can provide more stable and cost-effective services when the demand level is higher. The results also validate that the lockers are well utilized to facilitate parcel transfers. Regular drivers are needed to service some regions with low crowd-shipper supply.

Shang Zhang is a Master of Applied Science student at the University of Toronto Department of Civil & Mineral Engineering working under the supervision of Professor Matthew J. Roorda. His main research interest centers on analyzing the travel behaviour of crowd-shippers and how to improve crowd-shipping operation. He is also keen on developing efficient optimization solutions for novel city logistics practices, such as autonomous vehicles and physical internet.

Presented by University of Toronto ITE Student Chapter, UT-ITE.

Free. All are welcome.

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