Collaborative Crowdsourced Vehicles for Last-Mile Delivery Application Using Hedonic Cooperative Games

In this paper, the problem of collaboration in crowdsourced last-mile delivery is addressed, where multiple crowdsourced vehicles cooperate to fulfill tasks. Collaborative crowdsourced frameworks allow recruited vehicles, referred to as workers, to perform shorter trips while expanding the geographi...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.82506-82520
Main Authors: Elsokkary, Nada, Singh, Shakti, Mizouni, Rabeb, Otrok, Hadi, Barada, Hassan
Format: Article
Language:English
Subjects:
Algorithms
Collaboration
Collaborative algorithm
Collaborative software
cooperative hedonic game
Costs
crowdsourced vehicles
Crowdsourcing
Game theory
Games
last-mile delivery
Pedestrians
Relay
Relaying
Relays
Resource management
task allocation
Task analysis
Vehicles
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Summary:In this paper, the problem of collaboration in crowdsourced last-mile delivery is addressed, where multiple crowdsourced vehicles cooperate to fulfill tasks. Collaborative crowdsourced frameworks allow recruited vehicles, referred to as workers, to perform shorter trips while expanding the geographic coverage. Existing solutions in collaborative, crowdsourced last-mile delivery solely maximize the task allocation without considering 1) cost factors such as travel distance and payoff and 2) the self-interest of crowdsourced workers. As a solution, we propose a hedonic cooperative game approach that determines delivery routes and assigns relaying vehicles by maximizing the average payoff per kilometer, where payoffs are based on task contributions. Specifically, the proposed algorithm, hedonic crowd relay assignment (HCRA), uses the Nash equilibria of a series of hedonic games as the basis for the task allocation. To compute the workers' preference lists, HCRA relies on crowd relay breadth-first search (CR-BFS) to find a set of potential routes for task completion, given the constraints of the vehicles. The proposed solution is compared to a benchmark, and the results demonstrate that a more efficient and scalable solution is achieved using HCRA, where both the workers' total payoffs and average payoff per kilometer are increased, even with increasing numbers of vehicles, tasks, and relays.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3411156