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Artificial Divide: The New Challenge of Human-Artificial Performance in Logistics

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Innovative Produkte und Dienstleistungen in der Mobilität

Zusammenfassung

Logistics and supply chain management are undergoing a fast change due to technological but also social and market evolutions within the global economy [1], [2], [3], [7], [8], [16]. Autonomy, based on artificial intelligence as well as customer demands regarding customization, cost effectiveness and sustainability are the main driving forces behind these developments [5], [6], [9], [12].

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References

  • [1] Bloemhof JM, van der Vorst JG, Bastl M, Allaoui H (2015) Sustainability assessment of food chain logistics. International Journal of Logistics Research and Applications 18 (2):101‐117.

    Google Scholar 

  • [2] Fors C, Kircher K, Ahlström C (2015) Interface design of eco‐driving support systems – Truck drivers’ preferences and behavioural compliance. Transportation Research Part C 58:706‐720.

    Google Scholar 

  • [3] Gingerich K, Maoh H, Anderson W (2016) Classifying the purpose of stopped truck events: An application of entropy to GPS data. Transportation Research Part C 64:17‐27.

    Google Scholar 

  • [4] Koo J, Kwac J, Ju W, Steinert M, Leifer L, Nass C (2015) Why did my car just do that? Explaining semi‐autonomous driving actions to improve driver understanding, trust, and performance. International Journal on Interactive Design and Manufacturing 9 (4):269‐275.

    Google Scholar 

  • [5] LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521: 436‐444, doi:10.1038/nature14539.

  • [6] Lee H‐D, Lee B‐K, Kim W‐S, Han J‐S, Shin K‐S, Han C‐S (2014) Human–robot cooperation control based on a dynamic model of an upper limb exoskeleton for human power amplification. Mechatronics 24:168‐176.

    Google Scholar 

  • [7] Manuj I, Sahin F (2011) A model of supply chain and supply chain decision‐making complexity. International Journal of Physical Distribution & Logistics Management 41 (5):511‐549.

    Google Scholar 

  • [8] Nuzzolo A, Comi A (2014) A system of models to forecast the effects of demographic changes on urban shop restocking. Research in Transportation Business & Management 11:142‐151.

    Google Scholar 

  • [9] Phan M-H, Kim KH (2015) Negotiating truck arrival times among trucking companies and a container terminal. Transportation Research Part E 75:132-144.

    Google Scholar 

  • [10] Rauffet P, Lassalle J, Leroy B, Coppin G, Chauvina C (2015) The TAPAS project: facilitating cooperation in hybrid combat air patrols including autonomous UCAVs. 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015). Procedia Manufacturing 3: 974-981.

    Google Scholar 

  • [11] Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Networks 61:85-117.

    Google Scholar 

  • [12] Torabi SA, Hassini E, Jeihoonian, M (2015) Fulfillment source allocation, inventory transshipment, and customer order transfer in e‐tailing. Transportation Research Part E: Logistics and Transportation Review 79:128‐144.

    Google Scholar 

  • [13] Tsuji S, Aburatani H (2015) Deep learning for the large‐scale cancer data analysis. In: Cancer Research 75(22 Suppl 2): B1‐08.

    Google Scholar 

  • [14] Turing A, Braithwaite R, Jefferson G, Newman M (1952) Can automatic calculation machines be said to think? In: Copeland BJ (ed.) (2004) The essential Turing – Seminal writings in computing, logic, philosophy, artificial intelligence and artificial life, Oxford University Press, Oxford: 487‐505.

    Google Scholar 

  • [15] Weyer J, Fink RD, Adelt F (2015) Human‐machine cooperation in smart cars: An empirical investigation of the loss‐of‐control thesis. Safety Science 72:199‐208.

    Google Scholar 

  • [16] Zijm WMH, Klumpp M (2016) Logistics and supply chain management: Trends and developments, Springer, Berlin Heidelberg: 1‐20.

    Google Scholar 

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Authors and Affiliations

  1. FOM ild, Leimkugelstr. 6, 45141, Essen, Deutschland

    Prof. Dr. Matthias Klumpp

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  1. Prof. Dr. Matthias Klumpp
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Correspondence to Matthias Klumpp .

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Editors and Affiliations

  1. Universität Duisburg-Essen , Duisburg, Germany

    Heike Proff

  2. Universität Duisburg-Essen , Duisburg, Germany

    Thomas Martin Fojcik

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Klumpp, M. (2017). Artificial Divide: The New Challenge of Human-Artificial Performance in Logistics. In: Proff, H., Fojcik, T. (eds) Innovative Produkte und Dienstleistungen in der Mobilität. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-18613-5_37

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  • DOI: https://doi.org/10.1007/978-3-658-18613-5_37

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  • Publisher Name: Springer Gabler, Wiesbaden

  • Print ISBN: 978-3-658-18612-8

  • Online ISBN: 978-3-658-18613-5

  • eBook Packages: Business and Economics (German Language)

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