Crowdsourced Delivery

Setzke, David Soto; Pflügler, Christoph; Schreieck, Maximilian; Fröhlich, Sven; Wiesche, Manuel; Krcmar, Helmut

doi:10.1007/978-3-658-21214-8_17

Cited by 16 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pourrahmani and Jaller (2021) give an overview of the operational challenges and research opportunities that exist in this field. One of these operational challenges is the matching of parcels to crowd-shippers which has been studied by, among others, Li et al (2014) andSoto Setzke et al (2017). We also note the similarities with matching problems in ride-sharing (Masoud and Jayakrishnan (2017)), ride-hailing (Yan et al (2020) and carpooling (Wang et al (2018)).…”

Section: Literature Reviewmentioning

confidence: 85%

A Continuum Approximation Approach to the Hub Location Problem in a Crowd-Shipping System

Stokkink¹,

Geroliminis²

2022

Preprint

View full text Add to dashboard Cite

Last-mile delivery in the logistics chain contributes to emissions and increased congestion. Crowd-shipping is a sustainable and low-cost alternative to traditional delivery, but relies heavily on the availability of occasional couriers. In this work, we propose a hub-based crowd-shipping system that aims to attract sufficient potential crowd-shippers to serve a large portion of the demand for small parcels. While small-scale versions of this problem have been recently addressed, a scaling to larger instances significantly complexifies the problem. A heuristic approach based on continuum approximation is designed to evaluate the quality of a potential set of hub locations. By combining an efficient and accurate approximation method with a large neighborhood search heuristic, we are able to efficiently find a good set of hub locations, even for large scale networks. Furthermore, on top of determining good hub locations, our methods allow to identify the expected number of delivered parcels in every region, which can be used to design a smart dynamic assignment strategy. A case study on the Washington DC network shows that hubs are built at locations that are both geographically central, but most importantly are popular origins for crowd-shippers. The optimal number of hubs is mainly dependent on the marginal number of parcels that can be served by crowd-shippers from a specific hub, relative to the costs involved in opening that hub. The performance of our algorithm is close to that of a simulation-optimization algorithm, yet being up to 25 times faster. Thereby, the results show a dynamic assignment policy based on continuum approximation estimates outperforms existing assignment strategies.

show abstract

Section: Literature Reviewmentioning

confidence: 85%

A Continuum Approximation Approach to the Hub Location Problem in a Crowd-Shipping System

Stokkink¹,

Geroliminis²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Many publications focus on crowdshipping in general, but few papers deal with optimization models for matching ODs and demand. Early papers make highly simplifying assumptions, for example, one request per trip [35, 36]. Archetti et al [1] were the first to model the problem as VRP, but still allow only one request per OD trip.…”

Section: Discussionmentioning

confidence: 99%

“…The following publications consider ODs fulfilling delivery requests but omit the option of using TPs. Soto Setzke et al [35] propose a matching algorithm based on routes and time feasibility modeled as min‐cost max‐flow model. Wang et al [36] provide a similar approach with the difference that all parcels have to be delivered and each OD is allowed to make multiple deliveries.…”

Section: Related Literaturementioning

confidence: 99%

Crowdsourced logistics: The pickup and delivery problem with transshipments and occasional drivers

Voigt

Kühn

2021

Networks

View full text Add to dashboard Cite

This article considers a setting in which a courier, express, and parcel service provider operates a fleet of vehicles with regular drivers (RDs) to ship parcels from pickup to delivery points. Additionally, the company uses a platform where occasional drivers (ODs) offer their willingness to take on requests that are on or near the route they had originally planned. There exist transshipment points (TPs) to better integrate these ODs. ODs or RDs may transfer load at these predetermined TPs. The problem is modeled as a mixed-integer programming model and called pickup and delivery problem with transshipments and occasional drivers (PDPTOD). We develop a solution approach based on an adaptive large neighborhood search. The article provides insights on how the number and location of TPs impact the cost advantages achieved by integrating ODs. It also shows that the cost savings are highly sensitive to the assumed flexibility and compensation scheme.

show abstract

“…Several researchers extended the crowdshipping problem to a large-scale network problem ( 16 , 17 ). Wang et al assigned delivery jobs to potential workers in real time with the objective of minimizing additional travel distance and proposed solutions to solve the model for a large network ( 16 ).…”

Section: Related Workmentioning

confidence: 99%

Prioritizing Outlier Parcels for Public Transport-Based Crowdshipping in Urban Logistics

Zhang

Cheah

2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

The continuous growth of e-commerce puts pressure on logistic service providers to fulfill more parcel deliveries. Concurrently, there are increasing calls from governments and society to carry this out sustainably. Crowdshipping is one possible innovative logistic service that addresses these challenges. Crowdshipping enlists members of the public to fulfill parcel deliveries, ideally en route along their pre-committed journeys. In this paper, we consider a setting where public transport passengers can serve as crowdshippers and propose a comprehensive framework to organize the scheme. Firstly, outlier parcels are identified as being suitable for crowdshipping. Then, these outlying parcels are matched with crowdshippers, who pick up parcels from a set of selected parcel lockers. We also investigate the viability of crowdshipping with real-world data. By comparing a carrier’s performance to a base case without crowdshipping, the results show that delivery vehicle kilometers traveled and associated carbon dioxide emissions can reduce by up to 20%. A total of 11% of parcels can be redirected to be delivered via crowdshipping. Crowdshipping using public transport has the potential to be a sustainable way to fulfill urban logistics in a dense city.

show abstract

Crowdsourced Delivery

Cited by 16 publications

References 17 publications

A Continuum Approximation Approach to the Hub Location Problem in a Crowd-Shipping System

A Continuum Approximation Approach to the Hub Location Problem in a Crowd-Shipping System

Crowdsourced logistics: The pickup and delivery problem with transshipments and occasional drivers

Prioritizing Outlier Parcels for Public Transport-Based Crowdshipping in Urban Logistics

Contact Info

Product

Resources

About