Supply, demand, operations, and management of crowd-shipping services: A review and empirical evidence

Abstract Crowd-shipping promises social, economic, and environmental benefits covering a range of stakeholders. Yet, at the same time, many crowd-shipping initiatives face multiple barriers, such as network effects, and concerns over trust, safety, and security. This paper reviews current practice, academic research, and empirical case studies from three pillars of supply, demand, and operations and management. Drawing on the observed gaps in practice and scientific research, we provide several avenues for promising areas of applications, operations and management, as well as improving behavioral and societal impacts to create and enable a crowd-shipping system that is complex, yet, integrated, dynamic and sustainable.

[1]  Leo G. Kroon,et al.  Crowdsourced Delivery - A Dynamic Pickup and Delivery Problem with Ad Hoc Drivers , 2016, Transp. Sci..

[2]  Hajo A. Reijers,et al.  An adaptive large neighborhood search heuristic for the share-a-ride problem , 2016, Comput. Oper. Res..

[3]  Oliver Kunze,et al.  Replicators, Ground Drones and Crowd Logistics A Vision of Urban Logistics in the Year 2030 , 2016 .

[4]  Tom Van Woensel,et al.  A scenario-based planning for the pickup and delivery problem with time windows, scheduled lines and stochastic demands , 2016 .

[5]  Yu Nie,et al.  Crowdsourced Urban Package Delivery , 2017 .

[6]  Frank Teuteberg,et al.  Crowd Logistics − A Literature Review and Maturity Model , 2015 .

[7]  D. Woodard,et al.  Dynamic pricing and matching in ride‐hailing platforms , 2018, Naval Research Logistics (NRL).

[8]  Bruce L. Golden,et al.  The vehicle routing problem with drones: several worst-case results , 2017, Optim. Lett..

[9]  Lu Zhen,et al.  A Crowdsourcing Matching and Pricing Strategy in Urban Distribution System , 2018, IIMSS.

[10]  R. Jayakrishnan,et al.  A Real-Time Algorithm to Solve the Peer-to-Peer Ride-Matching Problem in a Flexible Ridesharing System , 2017 .

[11]  Chao Chen,et al.  Using the Crowd of Taxis to Last Mile Delivery in E-Commerce: a methodological research , 2015, SOHOMA.

[12]  João Pedro Pedroso,et al.  Stochastic last-mile delivery with crowdshipping , 2018 .

[13]  Michael Lettenmeier,et al.  Transport reduction by crowdsourced deliveries – a library case in Finland , 2016 .

[14]  Martin W. P. Savelsbergh,et al.  Vehicle routing with roaming delivery locations , 2017 .

[15]  Christine Bauer,et al.  "Crowd logistics": the contribution of social crowds in logistics activities , 2016, Int. J. Web Inf. Syst..

[16]  Mickael Briffaz,et al.  Crowd-shipping in Geneva Exploratory and descriptive study of Crowd-shipping , 2016 .

[17]  Ray Y. Zhong,et al.  A crowdsourcing solution to collect e-commerce reverse flows in metropolitan areas , 2015 .

[18]  Kyo Suh,et al.  Leveraging socially networked mobile ICT platforms for the last-mile delivery problem. , 2012, Environmental science & technology.

[19]  Shu-Chu Liu,et al.  A heuristic method for the inventory routing and pricing problem in a supply chain , 2011, Expert Syst. Appl..

[20]  Guido Perboli,et al.  Parcel delivery in urban areas: Opportunities and threats for the mix of traditional and green business models , 2019, Transportation Research Part C: Emerging Technologies.

[21]  Dong Li,et al.  Ride-matching and routing optimisation: Models and a large neighbourhood search heuristic , 2018, Transportation Research Part E: Logistics and Transportation Review.

[22]  Alireza Ermagun,et al.  Studying determinants of crowd-shipping use , 2018, Travel Behaviour and Society.

[23]  Marie Schmidt,et al.  Dynamic Programming Approaches for the Traveling Salesman Problem with Drone , 2017 .

[24]  Ali Shamshiripour,et al.  Performance analysis of crowd-shipping in urban and suburban areas , 2019, Transportation.

[25]  Roel Gevaers.  Evaluation of innovations in B2C last mile, B2C reverse & waste logistics , 2013 .

[26]  Jan Brinkmann,et al.  Anticipatory Planning for Courier, Express and Parcel Services , 2015 .

[27]  Gilbert Laporte,et al.  The dial-a-ride problem: models and algorithms , 2006, Ann. Oper. Res..

[28]  Eric Horvitz,et al.  Crowdphysics: Planned and Opportunistic Crowdsourcing for Physical Tasks , 2013, ICWSM.

[29]  Terry L. Esper,et al.  Designing crowdsourced delivery systems: The effect of driver disclosure and ethnic similarity , 2018 .

[30]  R. Cervero,et al.  Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco , 2016 .

[31]  Cathy Macharis,et al.  Shipping outside the box. Environmental impact and stakeholder analysis of a crowd logistics platform in Belgium , 2018, Journal of Cleaner Production.

[32]  Alexander H. Hübner,et al.  Last mile fulfilment and distribution in omni-channel grocery retailing: a strategic planning framework , 2016 .

[33]  Marie Schmidt,et al.  Optimization Approaches for the Traveling Salesman Problem with Drone , 2016, Transp. Sci..

[34]  Aashwinikumar Devari,et al.  Crowdsourced last mile delivery using social network , 2016 .

[35]  Alex Wang,et al.  Integration of the Load-Matching and Routing Problem with Equipment Balancing for Small Package Carriers , 2007, Transp. Sci..

[36]  Wenyi Chen,et al.  Multi-hop driver-parcel matching problem with time windows , 2018 .

[37]  C. Macharis,et al.  Crowd logistics: an opportunity for more sustainable urban freight transport? , 2017, European Transport Research Review.

[38]  Jean-François Cordeau,et al.  Branch-and-Price for the Pickup and Delivery Problem with Time Windows and Scheduled Lines , 2018, Transp. Sci..

[39]  Jacob D. Leshno,et al.  Unbalanced Random Matching Markets: The Stark Effect of Competition , 2017, Journal of Political Economy.

[40]  Zhanwei Du,et al.  Pocket-switch-network based services optimization in crowdsourced delivery systems , 2017, Comput. Electr. Eng..

[41]  Heleen Buldeo Rai,et al.  Can the Crowd Deliver? Analysis of Crowd Logistics' Types and Stakeholder Support , 2018, City Logistics 3.

[42]  Christos A. Kontovas,et al.  Dynamic vehicle routing problems: Three decades and counting , 2016, Networks.

[43]  Xingshe Zhou,et al.  Recommending travel packages based on mobile crowdsourced data , 2014, IEEE Communications Magazine.

[44]  Ling-Chieh Kung,et al.  The Optimal Pricing Strategy for Two-Sided Platform Delivery In the Sharing Economy , 2017 .

[45]  Dunren Che,et al.  Real-time recommendation algorithms for crowdsourcing systems , 2017 .

[46]  A. Stathopoulos,et al.  Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects , 2017 .

[47]  Sudheer Ballare,et al.  Preliminary Investigation of a Crowdsourced Package Delivery System: A Case Study , 2018, City Logistics 3.

[48]  Walid Klibi,et al.  Planning and operating a shared goods and passengers on-demand rapid transit system for sustainable city-logistics , 2015 .

[49]  Qing He,et al.  Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers , 2017 .

[50]  Bin Guo,et al.  Personalized Travel Package With Multi-Point-of-Interest Recommendation Based on Crowdsourced User Footprints , 2016, IEEE Transactions on Human-Machine Systems.

[51]  Daqing Zhang,et al.  crowddeliver: Planning City-Wide Package Delivery Paths Leveraging the Crowd of Taxis , 2017, IEEE Transactions on Intelligent Transportation Systems.

[52]  Michel Gendreau,et al.  A review of dynamic vehicle routing problems , 2013, Eur. J. Oper. Res..

[53]  Helmut Krcmar,et al.  A Matching Algorithm for Dynamic Ridesharing , 2016 .

[54]  Hajo A. Reijers,et al.  The Share-a-Ride problem with stochastic travel times and stochastic delivery locations , 2016 .

[55]  Jakob Puchinger,et al.  A survey of models and algorithms for optimizing shared mobility , 2019, Transportation Research Part B: Methodological.

[56]  Tom Van Woensel,et al.  An adaptive large neighborhood search heuristic for the Pickup and Delivery Problem with Time Windows and Scheduled Lines , 2016, Comput. Oper. Res..

[57]  Michela Le Pira,et al.  Connected shared mobility for passengers and freight: Investigating the potential of crowdshipping in urban areas , 2017, 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

[58]  Riccardo Lozza.  Crowdsourcing logistics in e-commerce B2C : a model to evaluate costs in different scenarios , 2016 .

[59]  Martin W. P. Savelsbergh,et al.  Service and capacity planning in crowd-sourced delivery , 2019, Transportation Research Part C: Emerging Technologies.

[60]  Bo Zou,et al.  Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery , 2017 .

[61]  Tom Van Woensel,et al.  The pickup and delivery problem with time windows and scheduled lines , 2016, Transp. Sci..

[62]  Frank Teuteberg,et al.  Understanding and assessing crowd logistics business models – using everyday people for last mile delivery , 2017 .

[63]  Martin W. P. Savelsbergh,et al.  50th Anniversary Invited Article - City Logistics: Challenges and Opportunities , 2016, Transp. Sci..

[64]  Martin W. P. Savelsbergh,et al.  The Vehicle Routing Problem with Occasional Drivers , 2016, Eur. J. Oper. Res..

[65]  Bruce L. Golden,et al.  Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey , 2018, Networks.

[66]  Bruce L. Golden,et al.  The vehicle routing problem with drones: Extended models and connections , 2017, Networks.

[67]  M. Browne,et al.  The rise of on-demand ‘Instant Deliveries’ in European cities , 2017 .

[68]  Xiao Han,et al.  Taxi Exp: A Novel Framework for City-Wide Package Express Shipping via Taxi Crowd Sourcing , 2014, 2014 IEEE 11th Intl Conf on Ubiquitous Intelligence and Computing and 2014 IEEE 11th Intl Conf on Autonomic and Trusted Computing and 2014 IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops.

[69]  Jie Yang,et al.  Exploring Co-Modality Using On-Demand Transport Systems , 2016 .

[70]  James J. Murphy,et al.  A Meta-analysis of Hypothetical Bias in Stated Preference Valuation , 2005 .

[71]  Eileen M. Trauth,et al.  Getting work done: evaluating the potential of crowdsourcing as a model for business process outsourcing service delivery , 2013, SIGMIS-CPR '13.

[72]  David A. Hensher,et al.  Hypothetical bias, choice experiments and willingness to pay , 2010 .

[73]  Valentina Carbone,et al.  The Rise of Crowd Logistics: A New Way to Co‐Create Logistics Value , 2017 .

[74]  Hajo A. Reijers,et al.  The Share-a-Ride Problem: People and parcels sharing taxis , 2014, Eur. J. Oper. Res..

[75]  Alireza Ermagun,et al.  To bid or not to bid: An empirical study of the supply determinants of crowd-shipping , 2018, Transportation Research Part A: Policy and Practice.

[76]  Ray Y. Zhong,et al.  Using taxis to collect citywide E-commerce reverse flows: a crowdsourcing solution , 2017, Int. J. Prod. Res..

[77]  Fabien Lehuédé,et al.  Optimization of a city logistics transportation system with mixed passengers and goods , 2017, EURO J. Transp. Logist..