Abstract
How to best deliver goods to consumers has been a logistics question since time immemorial. However, almost all traditional delivery models involved a form of company employees, whether employees of the company manufacturing the goods or whether employees of the company transporting the goods. With the growth of the gig economy, however, a new model not involving company employees has emerged: relying on crowdsourced delivery. Crowdsourced delivery involves enlisting individuals to deliver goods and interacting with these individuals using the internet. In crowdsourced delivery, the interaction with the individuals typically occurs through a platform. Importantly, the crowdsourced couriers are not employed by the platform and this has fundamentally changed the planning and execution of the delivery of goods: the delivery capacity is no longer under (full) control of the company managing the delivery. We present the challenges this introduces, review how the research community has proposed to handle some of these challenges, and elaborate on the challenges that have not yet been addressed.
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References
Al Hla YA, Othman M, Saleh Y (2019) Optimising an eco-friendly vehicle routing problem model using regular and occasional drivers integrated with driver behaviour control. J Clean Prod 234:984–1001
Allahviranloo M, Baghestani A (2019) A dynamic crowdshipping model and daily travel behavior. Transp Res Part E 128(May):175–190
Alnaggar A, Gzara F, Bookbinder JH (2021) Crowdsourced delivery: a review of platforms and academic literature. Omega 98:102139
Archetti C, Bertazzi L (2021) Recent challenges in routing and inventory routing: e-commerce and last-mile delivery. Networks 77(2):255–268
Archetti C, Guerriero F, Macrina G (2021) The online vehicle routing problem with occasional drivers. Comput Oper Res 127:105144
Archetti C, Martin Savelsbergh M, Speranza G (2016) The vehicle routing problem with occasional drivers. Eur J Oper Res 254(2):472–480
Arslan AM, Agatz N, Kroon L, Zuidwijk R (2019) Crowdsourced delivery–a dynamic pickup and delivery problem with ad hoc drivers. Transp Sci 53(1):222–235
Atkinson S (2021) ’more than a job’: the food delivery co-ops putting fairness into the gig economy. https://www.theguardian.com/world/2021/may/11/more-than-a-job-the-meal-delivery-co-ops-making-the-gig-economy-fairer
Ausseil R, Pazour JA, Ulmer MW (2021) Supplier menus for dynamic matching in peer-to-peer transportation platforms. Working paper
Behrend M, Meisel F (2018) The integration of item-sharing and crowdshipping: can collaborative consumption be pushed by delivering through the crowd? Transp Res B Methodol 111:227–243
Behrend M, Meisel F, Fagerholt K, Andersson H (2019) An exact solution method for the capacitated item-sharing and crowdshipping problem. Eur J Oper Res 279(2):589–604
Behrendt A, Savelsbergh M, Wang H (2021) A prescriptive machine learning method for courier scheduling on crowdsourced delivery platforms. Optimization Online 8861
Boysen N, Emde S, Schwerdfeger S (2021) Crowdshipping by employees of distribution centers: optimization approaches for matching supply and demand. Eur J Oper Res 296:539–556
Boysen N, Fedtke S, Schwerdfeger S (2020) Last-mile delivery concepts: a survey from an operational research perspective. OR Spectrum 43:1–58
Cao J, Olvera-Cravioto M, Shen Z-J (2020) Last-mile shared delivery: a discrete sequential packing approach. Math Oper Res 45(4):1466–1497
Caplice C (2007) Electronic markets for truckload transportation. Prod Oper Manag 16(4):423–436
Chen C, Yang S, Wang Y, Guo B, Zhang D (2020) Crowdexpress: a probabilistic framework for on-time crowdsourced package deliveries. IEEE Trans Big Data. https://doi.org/10.1109/TBDATA.2020.2991152
Cheng S-F, Chen C, Kandappu T, Lau HC, Misra A, Jaiman N, Tandriansyah R, Koh D (2017) Scalable urban mobile crowdsourcing: handling uncertainty in worker movement. ACM Trans Intell Syst Technol (TIST) 9(3):1–24
Cleophas C, Cottrill C, Ehmke JF, Tierney K (2019) Collaborative urban transportation: recent advances in theory and practice. Eur J Oper Res 273(3):801–816
Dahle L, Andersson H, Marielle Christiansen M, Speranza G (2019) The pickup and delivery problem with time windows and occasional drivers. Comput Oper Res 109:122–133
Dai H, Liu P (2020) Workforce planning for O2O delivery systems with crowdsourced drivers. Ann Oper Res 291(1):219–245
Dayarian I, Savelsbergh M (2020) Crowdshipping and same-day delivery: employing in-store customers to deliver online orders. Prod Oper Manag 29(9):2153–2174
Ergun O, Kuyzu G, Savelsbergh M (2007) Reducing truckload transportation costs through collaboration. Transp Sci 41(2):206–221
Ermagun A, Punel A, Stathopoulos A (2020) Shipment status prediction in online crowd-sourced shipping platforms. Sustain Cities Soc 53:101950
Ermagun A, Shamshiripour A, Stathopoulos A (2020) Performance analysis of crowd-shipping in urban and suburban areas. Transportation 47:1955–1985
Ermagun A, Stathopoulos A (2018) To bid or not to bid: an empirical study of the supply determinants of crowd-shipping. Transp Res A Policy Pract 116:468–483
Ermagun A, Stathopoulos A (2020) Crowd-shipping delivery performance from bidding to delivering. Res Transp Bus Manag 41:100614
Figliozzi MA, Mahmassani HS, Jaillet P (2007) Pricing in dynamic vehicle routing problems. Transp Sci 41(3):302–318
Gdowska K, Viana A, Pedroso JP (2018) Stochastic last-mile delivery with crowdshipping. Transp Res Procedia. Elsevier, 30:90–100
Guo X, Jaramillo YJ, Bloemhof-Ruwaard J, Claassen GD (2019) On integrating crowdsourced delivery in last-mile logistics: a simulation study to quantify its feasibility. J Clean Prod 241:118365
Kafle N, Bo Zou JL (2017) Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery. Transp Res B Methodol 99:62–82
Keane J (2020) How the pandemic put food delivery firms in the limelight in 2020. https://www.forbes.com/sites/jonathankeane/2020/12/15/how-the-pandemic-put-food-delivery-firms-in-the-limelight-in-2020/?sh=5130cc805eeb
Kim Y, Mahmassani HS (1882) Jaillet P (2004) Dynamic truckload routing, scheduling, and load acceptance for large fleet operation with priority demands. Transp Res Rec 1:120–128
Lafkihi M, Pan S, Ballot E (2019) Freight transportation service procurement: a literature review and future research opportunities in omnichannel e-commerce. Transp Res E Logist Transp Rev 125:348–365
Le TV, Stathopoulos A, Van Woensel T, Ukkusuri SV (2019) Supply, demand, operations, and management of crowd-shipping services: a review and empirical evidence. Transp Res C Emerg Technol 103:83–103
Le TV, Ukkusuri SV, Xue J, Van Woensel T (2021) Designing pricing and compensation schemes by integrating matching and routing models for crowd-shipping systems. Transp Res E Logist Transp Rev 149:102209
Lechtape M (2017) Pizzabote mit Vertrag. Süddeutsche Zeitung. https://www.sueddeutsche.de/wirtschaft/lieferdienste-pizzabote-mit-vertrag-1.3719192. Accessed 15 Aug 2018
Lei YM, Jasin S, Wang J, Deng H, Putrevu J (2020) Dynamic workforce acquisition for crowdsourced last-mile delivery platforms. SSRN
Levingston I (2021) Billions in VC money spell the end of the late-night beer run. https://www.bloomberg.com/news/articles/2021-05-26/gopuff-uber-delivery-hero-other-startups-enter-instant-delivery-business
Macrina G, Di Puglia L, Pugliese FG, Laporte G (2020) Crowd-shipping with time windows and transshipment nodes. Comput Oper Res 113:104806
Marshall M (2020) Snow storm creates food delivery business boom, but can put delivery drivers at risk. https://spectrumnews1.com/oh/columbus/news/2021/02/16/snow-storm-creates-food-delivery-business-boom
Miller J, Nie Y, Liu X (2020) Hyperpath truck routing in an online freight exchange platform. Transp Sci 54(6):1676–1696
Mofidi SS, Pazour JA (2019) When is it beneficial to provide freelance suppliers with choice? A hierarchical approach for peer-to-peer logistics platforms. Transp Res B Methodol 126:1–23
Möhlmann M, Henfridsson O (2019) What people hate about being managed by algorithms, according to a study of Uber drivers. Harv Bus Rev 30
Moss R (2020) Gig economy: just eat offers couriers better terms. https://www.personneltoday.com/hr/gig-economy-just-east-offers-couriers-better-terms/
Nieto-Isazaa S, Fontaineb P, Minnera S (2021) The value of stochastic crowd resources and strategic location of mini-depots for last-mile delivery: a Benders decomposition approach. Optimization Online 8405
Pasquini L (2021) Delivery hero expects 2021 revenue to more than double. https://www.reuters.com/article/delivery-hero-results-idUSL8N2ML1FP
Powell WB (1987) An operational planning model for the dynamic vehicle allocation problem with uncertain demands. Transp Res B Methodol 21(3):217–232
Powell WB (1996) A stochastic formulation of the dynamic assignment problem, with an application to truckload motor carriers. Transp Sci 30(3):195–219
Punel A, Ermagun A, Stathopoulos A (2018) Studying determinants of crowd-shipping use. Travel Behav Soc 12:30–40
Punel A, Ermagun A, Stathopoulos A (2019) Push and pull factors in adopting a crowdsourced delivery system. Transp Res Rec 2673(7):529–540
Punel A, Stathopoulos A (2017) Modeling the acceptability of crowdsourced goods deliveries: role of context and experience effects. Transp Res E Logist Transp Rev 105:18–38
Rai HB, Verlinde S, Macharis C (2021) Who is interested in a crowdsourced last mile? A segmentation of attitudinal profiles. Travel Behav Soc 22:22–31
Rana P, Kan J (2021) For DoorDash and Uber Eats, the future is everything in about an hour. https://www.wsj.com/articles/for-doordash-and-uber-eats-the-future-is-everything-in-about-an-hour-11622453401
Regan AC, Mahmassani HS, Jaillet P (1996) Dynamic decision making for commercial fleet operations using real-time information. Transp Res Rec 1537(1):91–97
Santini A, Viana A, Klimentova X, Pedroso JP (2021) The probabilistic travelling salesman problem with crowdsourcing. Optimization Online 8563
Shen H, Lin J (2020) Investigation of crowdshipping delivery trip production with real-world data. Transp Res E Logist Transp Rev 143:102106
Shveda K (2021) How coronavirus is changing grocery shopping. https://www.bbc.com/future/bespoke/follow-the-food/how-covid-19-is-changing-food-shopping.html
Skålnes J, Dahle L, Andersson H, Christiansen M, Hvattum LM (2020) The multistage stochastic vehicle routing problem with dynamic occasional drivers. In: International conference on computational logistics. Springer, pp 261–276
Smith T (2021) The Europeans rethinking the gig economy model. https://sifted.eu/articles/rethinking-the-gig-economy/
Soper S (2020) Amazon drivers are hanging smartphones in trees to get more work. https://www.bloomberg.com/news/articles/2020-09-01/amazon-drivers-are-hanging-smartphones-in-trees-to-get-more-work
Sugar R (2021) When the pandemic ends, where will delivery go? it is supposedly the future of restaurants, but is it the future we still want? https://www.grubstreet.com/2021/03/future-of-delivery-nimbus.html
Tao J, Dai H, Jiang H, Chen W (2020) Dispatch optimisation in O2O on-demand service with crowd-sourced and in-house drivers. Int J Prod Res 59:6054–6068
Tsai M-T, Saphores J-D, Regan A (2011) Valuation of freight transportation contracts under uncertainty. Transp Res E Logist Transp Rev 47(6):920–932
Ulmer M, Savelsbergh M (2020) Workforce scheduling in the era of crowdsourced delivery. Transp Sci 54(4):1113–1133
Wang Y, Zhang D, Liu Q, Shen F, Lee LH (2016) Towards enhancing the last-mile delivery: an effective crowd-tasking model with scalable solutions. Transp Res E Log Transp Rev 93:279–293
Yıldız B (2021) Express package routing problem with occasional couriers. Transp Res C Emerg Technol 123:102994
Yıldız B (2021) Package routing problem with registered couriers and stochastic demand. Transp Res E Logist Transp Rev 147:102248
Yildiz B, Savelsbergh M (2019) Service and capacity planning in crowd-sourced delivery. Transp Res C Emerg Technol 100:177–199
Zhang Q, Liu Y, Fan Z-P, Li Z-L (2020) Model-based rolling matching strategy for crowdsourced drivers and delivery tasks considering uncertain transportation duration. Transp Res Rec 0361198120974364
Zhen L, Yiwei W, Wang S, Yi W (2021) Crowdsourcing mode evaluation for parcel delivery service platforms. Int J Prod Econ 235:108067
Acknowledgements
Marlin Ulmer’s work is funded by the DFG Emmy Noether Programme, Project 444657906. We gratefully acknowledge their support.
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Savelsbergh, M.W., Ulmer, M.W. Challenges and opportunities in crowdsourced delivery planning and operations. 4OR-Q J Oper Res 20, 1–21 (2022). https://doi.org/10.1007/s10288-021-00500-2
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DOI: https://doi.org/10.1007/s10288-021-00500-2