Task bundling effect in electric scooter charging platform

Abstract

This study proposes two innovative optimization-based bundling algorithms to offer attractive options to the decentralized workforce in the electric scooter-sharing platform. The applicability of bundles is raised in enticing workers to a side hustle system of collecting low-battery scooters for a reward per task. The proposed bundling strategy considers the domain-specific characteristics of the scooter charging industry, such as autonomous task selection of workers, depot-oriented workers, a bundle decision phase before the worker selection phase, and limited information on workers’ task preferences. Based on assumptions about worker behavior, the value maximizing bundling (VMB) model aims to generate bundles with a higher reward-to-distance ratio, while the probability maximizing bundling (PMB) model additionally considers the distance required to reach the bundle centroid from the worker depot. The effectiveness of these bundling strategies is evaluated through a series of simulation experiments. Findings suggest that bundles significantly improved scooter collection rates compared to non-bundling scenarios. Additionally, this strategy enhances workers’ profit margins. Scenario-based simulations further demonstrate conditions that amplified the impact of bundling on the overall worker capacity and scooter distribution patterns. Given the superior performance of the PMB model with optimal parameters and the consistent stability of the VMB model, the study offers actionable insights for managers considering the implementation of bundling strategies.