Crowdsourced Auction-Based Framework for Time-Critical and Budget-Constrained Last Mile Delivery

Abstract

This research addresses the problem of Last Mile Delivery (LMD) under time-critical and budget-constrained environments. Given the rapid growth of e-commerce worldwide, LMD has become a primary bottleneck to the efficiency of delivery services due to several factors, including travelling distance, service cost, and delivery time. Existing works mainly target optimizing travelled distance and maximizing gained profit; however, they do not consider time-critical and budget-limited tasks. The deployment of drones and the development of crowdsourcing platforms have provided various solutions to advance performance in LMD frameworks. Crowdsourced LMD frameworks offer many crowdworkers at varying locations ready to perform tasks instead of having a single point of departure. In addition, incorporating drones in delivery services allows for faster task completion due to their relatively high operation speed and independence of road infrastructure and overhead. This work proposes a Hybrid, Crowdsourced, Auction-based LMD (HCA-LMD) framework with a dynamic allocation mechanism for optimized delivery of time-sensitive and budget-limited tasks. HCA-LMD allocates tasks to workers as soon as they are submitted, given their urgency level and drop-off location, while considering the price, rating, and location of available workers. The proposed framework is deployed to a consortium blockchain for transparent and decentralized task allocation and worker management. This work was compared against three benchmarks to assess the framework’s performance in dynamic environments in terms of on-time deliveries, average delay, and profit. Extensive simulation results showed an outstanding performance of the proposed state-of-the-art HCA-LMD framework by accomplishing almost 92% on-time deliveries under varying time- and budget-constrained scenarios, outperforming the first benchmark in the on-time allocation rate by fulfilling an additional 24% of the tasks the benchmark failed, with around 50% drop in average delay time and increased the gained profit to up to 5.8 times when compared against the second benchmark. The On-chain deployment of the proposed allocation mechanism is evaluated against a recent blockchain solution for crowdsourced auction-based LMD. Results indicate a remarkable performance on HCA-LMD compared to the third benchmark in at least 24% improvement in the on-time successful deliveries and up to 71% in certain cases.

Date of Award	6 May 2024
Original language	American English
Supervisor	Hadi Otrok (Supervisor)