Crowdsourcing integration on the last mile delivery platform considering floating population data

Abstract

In recent years, the crowdsourcing concept has been applied to last mile logistics. This study contributes to this growing field of research by addressing crowdsourcing integration into last mile delivery. To consider crowdsourced delivery options, an individual crowdsourced fleet is characterized in terms of its delivery pricing. A mathematical model is proposed to determine the potential set of locations for terminals for delivery-dedicated and individual crowdsourced fleets, with the objective of minimizing the total last mile delivery cost when demand and delivery pricing change depending on the floating population of a given city. Through numerical experiments with the annual floating population data in Ulsan Metropolitan City in the Republic of Korea, we identify the optimal sets of terminals for the integrated last mile delivery platform consisting of delivery-dedicated and individual crowdsourced fleets. Results show that the proposed model significantly reduces total cost by employing crowd workers in highly populated areas rather than those merely positioned close to the final delivery destinations. Subsequently, a sensitivity analysis is conducted regarding three cost parameters that are likely to impact the effectiveness and efficiency of the last mile delivery process. We further analyze the cost and carbon emissions associated with integrating crowdsourcing into the last mile delivery platform as well as the strategic distribution of incentive pay to encourage the shift to electric vehicles for last mile deliveries. Lastly, we investigate the effects of different traffic conditions on the optimal solutions.