Crowdsourcing Joins Last Mile Delivery With 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.

A groundbreaking development in logistics delivery efficiency has emerged through the innovative work of Professor Sang Jin Kweon and his team in the Department of Industrial Engineering at UNIST. Their cutting-edge research has led to the creation of a logistics optimization methodology that seamlessly integrates crowdsourcing with annual floating population data, revolutionizing the field.

The methodology focuses on enhancing the last mile delivery process, the final step in delivering goods to customers, by strategically determining optimal terminal locations through crowdsourcing. This approach has showcased a remarkable 3.09% reduction in total logistics costs, marking a significant advancement in the industry.

By facilitating the coexistence of existing logistics workers and crowd workers, the efficiency of logistics transportation has been significantly improved. Unlike traditional methods, this study considered employment and travel costs that vary based on population density. By incorporating factors, such as carbon emissions and transportation modes, used by crowd workers, a more comprehensive and efficient logistics solution has been achieved.

Figure 1. A schematic image, showing the allocation of crowd workers at pickup stations for parcel deliveries in the (a) basic model and (b) proposed models.

The methodology was successfully applied in the Southern District of Ulsan, demonstrating its adaptability and effectiveness in real-world scenarios. Sensitivity analyses on crucial cost parameters further highlighted the robustness of the developed approach, showcasing its potential for widespread implementation.

The team's exploration extended to the strategic distribution of incentive payments to promote the adoption of electric vehicles in last mile delivery, emphasizing the importance of sustainable practices. By forecasting various traffic situations, the study has laid a solid foundation for agile responses in challenging logistics environments.

Figure 2. Results for the optimal locations of terminals for the integrated last mile delivery platform with floating population density.

First author Jaesung Kim emphasized the potential of crowdsourcing in enhancing last-mile logistics efficiency, while Professor Kweon highlighted the study's comprehensive approach to address emerging logistics trends. The team envisions further research to integrate diverse mobility technologies into the logistics platform, ensuring continuous innovation in the field.

The team's exploration extended to the strategic distribution of incentive payments to promote the adoption of electric vehicles in last mile delivery, emphasizing the importance of sustainable practices. By forecasting various traffic situations, the study has laid a solid foundation for agile responses in challenging logistics environments.

Figure 3. Illustrations depicting the optimal locations of terminals for the last mile delivery platform during the different traffic conditions.

Jaesung Kim, the first author of the study, emphasized the potential of crowdsourcing in enhancing last-mile logistics efficiency, while Professor Kweon highlighted the study's comprehensive approach to address emerging logistics trends. The team envisions further research to integrate diverse mobility technologies into the logistics platform, ensuring continuous innovation in the field.

The findings of this groundbreaking research have been published in the online version of Expert Systems with Applications in January 2024, with an upcoming publication expected in August 2024. This study received support from the National Research Foundation (NRF) of Korea and the Ministry of Science and ICT (MSIT), underscoring its significance in advancing the logistics industry towards a more efficient and sustainable future.

Journal Reference

Jaesung Kim, Sang Jin Kweon, Seong Wook Hwang, and Seokgi Lee, "Crowdsourcing integration on the last mile delivery platform considering floating population data," Expert Syst. Appl., (2024).