On integrating crowdsourced delivery in last-mile logistics: A simulation study to quantify its feasibility

Guo, Xuezhen; Jaramillo, Yngrid Jaqueline Lujan; Bloemhof-Ruwaard, Jacqueline; Claassen, G.D.H.

doi:10.1016/j.jclepro.2019.118365

Cited by 73 publications

(41 citation statements)

References 34 publications

(39 reference statements)

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“…Furthermore, how the size and mix between delivery vehicle types in the hybrid fleets might affect the cost-service trade-off have not yet been explored. Guo et al (2019) explore the process of integrating in-store shoppers into a fulfillment strategy to provide delivery service to e-commerce customers of a grocery store; however, the authors do not consider the phenomenon of hybrid delivery fleets. In addition, recruiting instore shoppers requires different recruiting and retention strategies than contracting to the crowd.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The empirical crowdsourced per-mile compensation amount ($3 per delivery) was obtained from GrubHub driver onboarding documents; the other two levels of this variable are theoretical levels used to explore system-level outcomes as a response to these levels. The expedited delivery cost was adapted from Guo et al (2019). Finally, the delivery locations were randomly selected from the addresses in the empirical dataset for each run across all scenarios in the simulation.…”

Section: Variables and Parametersmentioning

confidence: 99%

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Hybrid last mile delivery fleets with crowdsourcing: A systems view of managing the cost‐service trade‐off

Castillo

Bell

Mollenkopf

et al. 2021

J of Business Logistics

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Retail firms now offer same-day delivery via hybrid fleets that augment a privately owned delivery fleet with crowdsourced assets. As research on crowdsourced delivery continues to grow, it has implicitly assumed deliveries being conducted with an entirely crowdsourced fleet. In this study, we adopt a sociotechnical systems perspective to consider the phenomenon of combining privately owned delivery vehicles with crowdsourced assets into a hybrid fleet. In this exploratory effort, we examine how elements of the hybrid delivery system, namely, driver autonomy, compensation, fleet size, fleet mix, and demand intensity affect cost and service in last mile delivery. Using a multimethod simulation combining agent-based and discrete event techniques with home delivery data from a major US retail pharmacy, we show how the emergent outcomes of this hybrid delivery system are a consequence of the constituent elements' interdependence. Our experiments suggest a convex relationship between crowdsourced driver compensation and cost performance, such that low and high compensation amounts may actually increase unit delivery costs relative to a median compensation level, and a nonlinear negative relationship between compensation and fulfillment time. We find that these effects are moderated by fleet design (in terms of size and vehicle type mix) and order arrival rate intensity.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Variables and Parametersmentioning

confidence: 99%

Hybrid last mile delivery fleets with crowdsourcing: A systems view of managing the cost‐service trade‐off

Castillo

Bell

Mollenkopf

et al. 2021

J of Business Logistics

View full text Add to dashboard Cite

show abstract

“…They put forward various pruning technologies to significantly downsize the logistics network system and realize real-time delivery optimization, with the optimal transportation cost as the goal. Guo et al (2019) developed the transformation framework of urban lastmile delivery from a conventional logistics network to a crowdsourcing delivery system and proposed " five principles" of the framework. They conducted the simulation to verify the applicability of the proposed theoretical framework.…”

Section: Table 1 the Difference In Rural Last-mile Research Between Developing Countries And Developed Countriesmentioning

confidence: 99%

“…As mentioned in the introduction, whether crowdsourcing delivery can save costs is controversial, and the feasibility of its implementation in rural areas also remains to be studied. Guo et al (2019) pointed out that the smooth transition of urban last-mile deliver y f rom conventiona l log istics network to crowdsourcing delivery network belongs to the paradigm of socio-technical transformation. So it is the same for rural last-mile delivery, which requires transform the conventional logistics modes and enable villagers to enjoy the convenience of logistics and transportation technologies.…”

Section: Table 1 the Difference In Rural Last-mile Research Between Developing Countries And Developed Countriesmentioning

confidence: 99%

Investigation on the Last-Mile Delivery of Rural Logistics Under Crowdsourcing Transportation in China

2021

IJTTE

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Recently, the last-mile delivery problem of rural e-commerce has emerged and became a bottleneck issue, due to the booming development of rural e-commerce which drives the vigorous growth of upstream agricultural products and downstream industrial products. This paper takes rural areas of China as a case study to discuss the last-mile delivery cost under crowdsourcing logistics. Owing to the absence of theoretical guidance on rural crowdsourcing logistics, the paper firstly intends to explore the theoretical framework of rural crowdsourcing logistics in terms of multi-level perspective (MLP). Then, the couriers' willingness coefficient is introduced to develop a mathematical optimization model containing time windows and vehicle capacity constraints, and the heuristic algorithm is applied to solve the model. The final results indicate that the transportation cost and Vehicle Miles Traveled (VMT) can be saved 23.85% and 37.38% respectively, compared with conventional rural logistics. Besides, analysis indicates that under certain conditions the approach of crowdsourcing delivery can be economically competitive and has valuable implication for unchoking the last-mile delivery problem of other similar rural regions.

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“…Crowdsourced delivery is a logistics delivery service that is coordinated by a technology platform and designates the delivery tasks to the public to achieve benefits for all stakeholders ( 1–6 ). It is an important part of the sharing economy that provides people with the chance to share and redistribute the excess transportation capacity ( 7–9 ). Compared with traditional logistics, crowdsourced delivery can reduce the cost and time of delivery, improve the performance of delivery, and enhance the social resource utilization ( 10–16 ).…”

mentioning

confidence: 99%

Model-Based Rolling Matching Strategy for Crowdsourced Drivers and Delivery Tasks Considering Uncertain Transportation Duration

Zhang

Liu

Fan

et al. 2020

Transportation Research Record

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The matching of crowdsourced drivers and delivery tasks is an important decision problem for the crowdsourced delivery platform. Although the existence of uncertainty of transportation duration in logistics delivery has been verified, uncertain transportation duration has not been considered in previous studies on the matching of crowdsourced drivers and delivery tasks. This would lead to the limitation that the results of the existing methods cannot meet the time requirements of senders. In this case, the profit and customer satisfaction of the crowdsourced delivery platform would decrease. In this paper, a model-based rolling matching strategy to match crowdsourced drivers and delivery tasks considering uncertain transportation duration is proposed. In addition, it is assumed that the crowdsourced delivery platform also has some dedicated drivers to implement the delivery tasks that cannot be implemented by crowdsourced drivers. First, a simpler problem is described, which is to match crowdsourced drivers and delivery tasks considering uncertain transportation duration in a static data environment. Then, a model is proposed to solve the above problem. Based on the proposed model, this paper further proposes a rolling procedure to solve the problem in a data refreshing environment. Moreover, a heuristic algorithm is presented for combining multiple delivery tasks to solve the one-to-many matching. Finally, a case study and comparison are given to illustrate the validity and the contribution of the proposed matching strategy. The results show that the proposed matching strategy has a distinct advantage of cost savings.

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On integrating crowdsourced delivery in last-mile logistics: A simulation study to quantify its feasibility

Cited by 73 publications

References 34 publications

Hybrid last mile delivery fleets with crowdsourcing: A systems view of managing the cost‐service trade‐off

Hybrid last mile delivery fleets with crowdsourcing: A systems view of managing the cost‐service trade‐off

Investigation on the Last-Mile Delivery of Rural Logistics Under Crowdsourcing Transportation in China

Model-Based Rolling Matching Strategy for Crowdsourced Drivers and Delivery Tasks Considering Uncertain Transportation Duration

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