The role of individual compensation and acceptance decisions in crowdsourced delivery
Alim Bu\u{g}ra \c{C}{\i}nar, Wout Dullaert, Markus Leitner, Rosario, Paradiso, Stefan Waldherr

TL;DR
This paper models and optimizes crowdsourced delivery by explicitly considering how compensation influences driver acceptance, proposing a mixed-integer nonlinear model and an efficient solution algorithm to minimize expected costs.
Contribution
It introduces a novel model that incorporates acceptance probabilities dependent on compensation and provides an exact solution method with polynomial runtime under certain conditions.
Findings
The proposed model outperforms existing approaches in computational experiments.
The solution algorithm is efficient and scalable for large problem instances.
The approach remains effective in dynamic, real-time delivery scenarios.
Abstract
One of the recent innovations in urban distribution is crowdsourced delivery, where deliveries are made by occasional drivers who wish to utilize their surplus resources (unused transport capacity) by making deliveries in exchange for some compensation. The potential benefits of crowdsourced delivery include reduced delivery costs and increased flexibility (by scaling delivery capacity up and down as needed). The use of occasional drivers poses new challenges because (unlike traditional couriers) neither their availability nor their behavior in accepting delivery offers is certain. The relationship between the compensation offered to occasional drivers and the probability that they will accept a task has been largely neglected in the scientific literature. Therefore, we consider a setting in which compensation-dependent acceptance probabilities are explicitly considered in the process…
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Taxonomy
TopicsBlood donation and transfusion practices · Sharing Economy and Platforms · Outsourcing and Supply Chain Management
