Estimating small area demand for online package delivery

Fabusuyi, Tayo; Twumasi-Boakye, Richard; Broaddus, Andrea; Fishelson, James; Hampshire, Robert C.

doi:10.1016/j.jtrangeo.2020.102864

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…Using the number of locations, visiting area, distance, and dispersion, the travel time was predicted using different state-of-the-art regressors for last-mile delivery [33]. Also, regression models [34] are used to estimate the delivery demands of online orders.…”

Section: Capacity Planning With Machine Learningmentioning

confidence: 99%

“…In this capacity planning study, which the authors considered a multipoint pick-up and delivery problem with time intervals and transfers, they developed a crowd-sourced system in which the availability of the drivers to be able to deliver for a certain period is shared, and service requests are forwarded [36]. The authors of [34] used regression models to predict the delivery demands of orders placed over the Internet.…”

Section: Capacity Planning With Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

An Integrated Framework for Dynamic Vehicle Routing Problems with Pick-up and Delivery Time Windows and Shared Fleet Capacity Planning

Küp,

Cebeci,

Bayram

et al. 2024

Symmetry

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This paper proposes a novel route optimization framework to solve the problem of instant pick-up and delivery for e-grocery orders. The proposed framework extends the traditional time-windowed package delivery problem. We demonstrate the effectiveness of our approach for this integrated problem using actual delivery data from HepsiJet, a leading e-commerce logistics provider in Turkey. We first employ several machine learning algorithms and simulations to investigate the capacity of the courier. Subsequently, a dynamic route planning workflow is executed with a highly specialized and novel routing algorithm. Our proposed heuristic approach considers combined fleet operations for delivering regular packages originating from a central depot and dynamic e-grocery orders picked up at local supermarkets and delivered to the customers. The heuristic algorithm constitutes k-opt and node transfer operation variations customized for this integrated problem. We report the performance of our approach in problem instances from the literature and instances from HepsiJet’s daily operations, which we also publicly share as new route optimization problem instances. Our results suggest that, despite the more complex nature of the integrated problem, our proposed algorithm and solution framework produce more efficient and cost-effective solutions that offer additional business opportunities for companies such as HepsiJet. The computational analyses reveal that implementing our proposed approach yields significant efficiency gains and cost reductions for the company, with a distance reduction of over 30%, underscoring our approach’s effectiveness in achieving substantial cost savings and enhanced efficiency through integrating two distinct delivery operations.

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Section: Capacity Planning With Machine Learningmentioning

confidence: 99%

Section: Capacity Planning With Machine Learningmentioning

confidence: 99%

An Integrated Framework for Dynamic Vehicle Routing Problems with Pick-up and Delivery Time Windows and Shared Fleet Capacity Planning

Küp,

Cebeci,

Bayram

et al. 2024

Symmetry

View full text Add to dashboard Cite

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“…These studies can inform the organization of urban space, including roadway capacity planning, street design, zoning, and building management. Most studies of e-commerce travel demand characteristics rely on national or regional household surveys or original surveys of shopping-specific behavior ( 8–15 , 18 ). A single study by Rodrigue ( 20 ) examined delivery frequency trends using building delivery records.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Much of the recent research focuses on consumer shopping behavior and the associated demand for package delivery. Many studies have aimed to understand the impacts of demographic and/or built environment characteristics on package demands, which are then translated to vehicle or parking activity via relevant assumptions (8)(9)(10)(11)(12)(13)(14)(15). Several of these travel-behavior-focused studies specifically investigate passenger and freight travel demand trade-offs for instore versus online shopping activity (8,9,(16)(17)(18)(19).…”

Section: Literature Reviewmentioning

confidence: 99%

What Happens Before the Last Mile? Exploring a Package’s Journey

Tejada

Conway

2022

Transportation Research Record: Journal of the Transportation R

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Urban freight studies of e-commerce activities and impacts tend to focus on last-mile movements of goods. Little information is available to quantify e-commerce activity for residential deliveries of parcels upstream of the last mile. In this exploratory study, we investigate the usefulness of a novel type of data—individual package tracking records—to measure end-to-end e-commerce activity. Using a small case study, we measure distances, times, and speeds of service to characterize components of e-commerce supply chains and to understand related impacts on transportation networks. Our results detail the strengths, weaknesses, and challenges expected in using this type of data, as well as provide insights for future data collection efforts. Overall, our results indicate that package tracking records do provide information that is potentially useful to inform transportation planning, but that further efforts are needed to establish appropriate technical and institutional frameworks to support large-scale analysis.

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