Skip to main content
SpringerLink
Log in

Artificial Intelligence in Urban Last Mile Logistics - Status Quo, Potentials and Key Challenges

  • Conference paper
  • First Online:
Dynamics in Logistics (LDIC 2022)

Part of the book series: Lecture Notes in Logistics ((LNLO))

Included in the following conference series:

Abstract

Artificial Intelligence (AI) has the potential to solve the sustainability and service issues of Urban Last Mile Logistics (ULML). High delivery costs, noisy and polluting traffic, bad working conditions and failed delivery attempts could be addressed by measures like AI-based demand forecasting, intelligent tour and route optimization or digital delivery assistance. However, there is little empirical evidence on the extent to which AI can do this. Thus, the purpose of this report is to elaborate the relevance of AI for solving ULML problems by identifying use cases, potentials and challenges of implementing AI in ULML planning and execution. Therefore, we conducted 15 explorative expert interviews with ULML companies and analyzed them using qualitative content analysis to obtain an initial orientation in this new empirical research field. The findings indicate, among others, that the ULML industry is in the very early stages of AI implementation and that there is relevant potential for efficiency and service improvement. However, one of the key challenges is the perceived high level of uncertainty about achieving economic benefits while having high investment and AI operating costs. The practical contribution of this paper is to provide guidance for ULML companies starting AI activities. The scientific contribution is to show the practical need for AI implementation and to derive concrete research needs for the development of suitable AI methods and algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Brabänder, C.: Die Letzte Meile. Definition, Prozess, Kostenrechnung und Gestaltungsfelder, 1st edn. Springer Gabler, Wiesbaden (2020). https://doi.org/10.1007/978-3-658-29927-9

  2. Schnedlitz, P., Lienbacher, E., Waldegg-Lindl, B., Waldegg-Lindl, M.: Last Mile: Die letzten – und teuersten – Meter zum Kunden im B2C ECommerce. In: Crockford, G., Ritschel, F., Schmieder, U.M. (eds.) Handel in Theorie und Praxis, pp. 249–273. Springer Gabler, Wiesbaden (2013). https://doi.org/10.1007/978-3-658-01986-0_10

    Chapter  Google Scholar 

  3. United Nations: World Urbanization Prospects, The 2019 Revision. Technical report, Department of Economic and Social Affairs, Population Division, New York (2020)

    Google Scholar 

  4. Handelsverband Deutschland – HDE e. V: Online Monitor 2021 (2021). https://einzelhandel.de/index.php?option=com_attachments&task=download&id=10572. Accessed 27 Aug 2021

  5. Bundesverband Paket und Expresslogistik e. V (BIEK): KEP-Studie 2021 – Analyse des Marktes in Deutschland (2021). https://www.biek.de/files/biek/downloads/papiere/BIEK_KEP-Studie_2021.pdf. Accessed 28 Aug 2021

  6. Seeck, S., Göhr, M.: Paketzustellung und Kundenservice – ein Widerspruch in sich? In: Digitales trifft Reales: 35. Deutscher Logistik-Kongress 17–19.10.2018, Kongressband, pp. 204–218. DVV Media Group, Hamburg (2018)

    Google Scholar 

  7. KoMoDo Website: Mikro-Depots und Lastenräder - klimaneutral auf der letzten Meile. https://www.komodo.berlin/. Accessed 28 Aug 2021

  8. HTW Berlin Website: Kundenorientierte Paketzustellung durch den Kiez-Boten (KOPKIB), https://www.htw-berlin.de/forschung/online-forschungskatalog/projekte/projekt/?eid=2747. Accessed 27 Aug 2021

  9. RWTH Aachen University Website: Automatisierte, nutzerorientierte Transportplattform für den urbanen Raum (UrbANT). https://www.itec.rwth-aachen.de/cms/ITEC/Forschung/Projekte/~cialq/UrbANT/. Accessed 27 Aug 2021

  10. Duarte, G., Rolim, C., Baptista, P.: How battery electric vehicles can contribute to sustainable urban logistics: a real-world application in Lisbon, Portugal. Sustain. Energy Technol. Assess. 15, 71–78 (2016)

    Google Scholar 

  11. Hofer, K., Flucher, S., Fellendorf, M., Schadler, M., Hafner, N.: Estimation of changes in customer’s mobility behaviour by the use of parcel lockers. Transp. Res. Procedia 47, 425–432 (2020)

    Article  Google Scholar 

  12. Richter, R., Söding, M., Christmann, G.: Logistik und Mobilität in der Stadt von morgen. Leibniz-Institut für Raumbezogene Sozialforschung (IRS), Erkner (2020)

    Google Scholar 

  13. Min, H.: Artificial intelligence in supply chain management: theory and applications. Int. J. Logist. Res. Appl. 13(1), 13–39 (2010)

    Article  Google Scholar 

  14. Straube, F.: Artificial Intelligence in Logistics and Supply Chain Management. 35. Deutscher Logistik-Kongress 17–19.10.2018 (2018). https://www.logistik.tu-ber-lin.de/fileadmin/fg2/PDFs/Stellenanzeigen/Frank_Straube_Vortrag_Artificial_Intelligence_DLK.pdf. Accessed 27 Aug 2021

  15. Thaller, C., Telake, M., Clausen, U., Dahmen, B., Leerkamp, B.: KEP-Verkehr in urbanen Räumen. In: Proff, H., Fojcik, T. (eds.) Innovative Produkte und Dienstleistungen in der Mobilität. Springer Gabler, Wiesbaden (2017). https://doi.org/10.1007/978-3-658-18613-5_28

  16. Balachandra, K., Perera, H.N., Thibbotuwawa, A.: Human factor in forecasting and behavioral inventory decisions: a system dynamics perspective. In: Freitag, M., Haasis, H.-D., Kotzab, H., Pannek, J. (eds.) LDIC 2020. LNL, pp. 516–526. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44783-0_48

    Chapter  Google Scholar 

  17. Kersten, W., Seiter, M., von See, B., Hackius, N., Maurer, T.: Chancen der digitalen Transformation. Trends und Strategien in Logistik und Supply Chain Management. Bundesvereinigung Logistik (BVL), Hamburg (2017)

    Google Scholar 

  18. Voß, P.H. (ed.): Logistik – die unterschätzte Zukunftsindustrie. Springer, Wiesbaden (2020). https://doi.org/10.1007/978-3-658-27317-0

    Book  Google Scholar 

  19. Straube, F., Junge, A.L.: Smarte Konzepte für zukunftsfähige urbane Logistik- und Verkehrssysteme. Internationales Verkehrswesen 71(2), 35 (2019)

    Google Scholar 

  20. Witten, P., Schmidt, C.: Globale Trends und die Konsequenzen für die Logistik der letzten Meile. In: Schröder, M., Wegner, K. (eds.) Logistik im Wandel der Zeit – Von der Produktionssteuerung zu vernetzten Supply Chains, pp. 303–319. Springer Gabler, Wiesbaden (2019). https://doi.org/10.1007/978-3-658-25412-4_14

  21. Hölter, A.-K., Ninnemann, J.: Effizienz auf der letzten Meile – Optimierung der Schnittstellen zwischen Kunde, Logistik und Onlinehandel. J. für Mobilität und Verkehr 5, 30–34 (2020)

    Article  Google Scholar 

  22. Jonas, D., Miller, F.-J., Seng, D.: Von gleich auf jetzt – Same Day Delivery am Beispiel von Liefery. In: Heinemann, G., Gehrckens, HMathias, Täuber, T. (eds.) Handel mit Mehrwert, pp. 473–493. Springer, Wiesbaden (2019). https://doi.org/10.1007/978-3-658-21692-4_21

    Chapter  Google Scholar 

  23. Wennker, P.: Künstliche Intelligenz – Eine kurze Geschichte. In: Wennker, P. (ed.) Künstliche Intelligenz in der Praxis: Anwendung in Unternehmen und Branchen: KI wettbewerbs- und zukunftsorientiert einsetzen, pp. 1–8. Springer Fachmedien Wiesbaden, Wiesbaden (2020). https://doi.org/10.1007/978-3-658-30480-5_1

    Chapter  Google Scholar 

  24. Özmen, M., Aydoğan, E.K.: Robust multi-criteria decision making methodology for real life logistics center location problem. Artif. Intell. Rev. 53(1), 725–751 (2019). https://doi.org/10.1007/s10462-019-09763-y

    Article  Google Scholar 

  25. Wu, H., Shao, D., Ng, W.S.: Locating self-collection points for last-mile logistics using public transport data. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9077, pp. 498–510. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18038-0_39

    Chapter  Google Scholar 

  26. Guo, H., Guo, C., Xu, B., Xia, Y., Sun, F.: MLP neural network-based regional logistics demand prediction. Neural Comput. Appl. 33(9), 3939–3952 (2020)

    Article  Google Scholar 

  27. Leung, K.H., Mo, D.Y., Ho, G.T.S., Wu, C.H., Huang, G.Q.: Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology. Ind. Manag. Data Syst. 120(6), 1149–1174 (2020)

    Article  Google Scholar 

  28. Liu, Y.: A progressive motion-planning algorithm and traffic flow analysis for high-density 2D traffic. Transp. Sci. 53(6), 1501–1525 (2019)

    Article  Google Scholar 

  29. Essien, A., Petrounias, I., Sampaio, P., Sampaio, S.: A deep-learning model for urban traffic flow prediction with traffic events mined from twitter. World Wide Web 24(4), 1345–1368 (2020)

    Article  Google Scholar 

  30. Huang, Y., Zhao, L., Powell, W.B., Tong, Y., Ryzhov, I.O.: Optimal learning for urban delivery fleet allocation. Transp. Sci. 53(3), 623–641 (2019)

    Article  Google Scholar 

  31. Liu, S., He, L., Max Shen, Z.-J.: On-time last mile delivery: order assignment with travel time predictors. Forthcoming Manage. Sci., 1–55 (2018)

    Google Scholar 

  32. Edelkamp, S., Gath, M., Greulich, C., Humann, M., Herzog, O., Lawo, M.: Monte-Carlo tree search for logistics. In: Clausen, U., Friedrich, H., Thaller, C., Geiger, C. (eds.) Commercial Transport. LNL, pp. 427–440. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-21266-1_28

    Chapter  Google Scholar 

  33. Shi, H., Sun, L., Teng, Y., Hu, X.: An online intelligent vehicle routing and scheduling approach for B2C e-commerce urban logistics distribution. Procedia Comput. Sci. 159, 2533–2542 (2019)

    Article  Google Scholar 

  34. Snoeck, A., Merchán, D., Winkenbach, M.: Route learning: a machine learning-based approach to infer constrained customers in delivery routes. Transp. Res. Procedia 46, 229–236 (2020)

    Article  Google Scholar 

  35. Tan, Y., Deng, L., Li, L., Yuan, F.: The capacitated pollution routing problem with pickup and delivery in the last mile. Asia Pac. J. Mark. Logist. 31(4), 1193–1215 (2019)

    Article  Google Scholar 

  36. Salama, M., Srinivas, S.: Joint optimization of customer location clustering and drone-based routing for last-mile deliveries. Transp. Res. Part C Emerg. Technol. 114, 620–642 (2020)

    Article  Google Scholar 

  37. Poeting, M., Schaudt, S., Clausen, U.: Simulation of an optimized last-mile parcel delivery network involving delivery robots. In: Clausen, U., Langkau, S., Kreuz, F. (eds.) ICPLT 2019. LNL, pp. 1–19. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13535-5_1

    Chapter  Google Scholar 

  38. Giret, A., Carrascosa, C., Julian, V., Rebollo, M., Botti, V.: A crowdsourcing approach for sustainable last mile delivery. Sustainability 10(12), 4563 (2018)

    Article  Google Scholar 

  39. Wang, F., Zhu, Y., Wang, F., Liu, J., Ma, X., Fan, X.: Car4Pac: last mile parcel delivery through intelligent car trip sharing. IEEE Trans. Intell. Transp. Syst. 21(10), 4410–4424 (2020)

    Article  Google Scholar 

  40. Quintero-Araujo, C.L., Gruler, A., Juan, A.A., de Armas, J., Ramalhinho, H.: Using simheuristics to promote horizontal collaboration in stochastic city logistics. Prog. Artif. Intell. 6(4), 275–284 (2017)

    Article  Google Scholar 

  41. Zhao, B., Teo, Y.S., Ng, W.S., Ng, H.H.: Data-driven next destination prediction and ETA improvement for urban delivery fleets. IET Transp. Syst. 13(11), 1624–1635 (2019)

    Article  Google Scholar 

  42. Hughes, S., Moreno, S., Yushimito, W.F., Huerta-Cánepa, G.: Evaluation of machine learning methodologies to predict stop delivery times from GPS data. Transp. Res. Part C Emerg. Technol. 109, 289–304 (2019)

    Article  Google Scholar 

  43. Merchán, D., Winkenbach, M.: An empirical validation and data-driven extension of continuum approximation approaches for urban route distances. Networks 73, 418–433 (2019)

    Article  MathSciNet  Google Scholar 

  44. Gmira, M., Gendreau, M., Lodi, A., Potvin, J.-Y.: Travel speed prediction based on learning methods for home delivery. EURO J. Transp. Logist. 9(4), 100006 (2020)

    Article  Google Scholar 

  45. Kretzschmar, J., Gebhardt, K., Theiß, C., Schau, V.: Range prediction models for e-vehicles in urban freight logistics based on machine learning. In: Tan, Y., Shi, Y. (eds.) Data Mining and Big Data, DMBD 2016. LNCS, vol. 9714. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40973-3_17

  46. Tian, Z., Zhong, R.Y., Barenji, A.V., Wang, Y.T., Li, Z., Rong, Y.: A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics. Int. J. Prod. Res. 59(7), 2229–2249 (2020)

    Article  Google Scholar 

  47. Bogner, A., Littig, B., Menz, W.: Interviews mit Experten. Springer Fachmedien Wiesbaden, Wiesbaden (2014). https://doi.org/10.1007/978-3-531-19416-5

    Book  Google Scholar 

  48. Europäische Union: Empfehlung der Kommission vom 6. Mai 2003 betreffend die Definition der Kleinstunternehmen sowie der kleinen und mittleren Unternehmen. Bekannt gegeben unter Aktenzeichen K(2003) 1422, Amtsblatt der Europäischen Union (2003)

    Google Scholar 

  49. Gläser, J., Laudel, G.: Experteninterviews und qualitative Inhaltsanalyse, 4th edn. VS Verlag für Sozialwissenschaften, Wiesbaden (2010)

    Google Scholar 

  50. Mayring, P., Fenzl, T.: Qualitative Inhaltsanalyse. In: Baur, N., Blasius, J. (eds.) Handbuch Methoden der empirischen Sozialforschung, pp. 543–556. Springer, Wiesbaden (2014). https://doi.org/10.1007/978-3-531-18939-0_38

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Applied Sciences Berlin, Berlin, Germany

    Maximilian Engelhardt, Stephan Seeck & Ben Geier

Authors
  1. Maximilian Engelhardt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephan Seeck
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ben Geier
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maximilian Engelhardt .

Editor information

Editors and Affiliations

  1. Production Systems and Logistics, University of Bremen, Bremen, Germany

    Michael Freitag

  2. Global Supply Chain Management, University of Bremen, Bremen, Germany

    Aseem Kinra

  3. Logistics Management, University of Bremen, Bremen, Germany

    Herbert Kotzab

  4. Combinatorial Optimization and Logistics, University of Bremen, Bremen, Germany

    Nicole Megow

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Engelhardt, M., Seeck, S., Geier, B. (2022). Artificial Intelligence in Urban Last Mile Logistics - Status Quo, Potentials and Key Challenges. In: Freitag, M., Kinra, A., Kotzab, H., Megow, N. (eds) Dynamics in Logistics. LDIC 2022. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-031-05359-7_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-05359-7_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05358-0

  • Online ISBN: 978-3-031-05359-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation