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Cited by 338Impact of AI and Big Data on Business and SocietyTransport and Logistics InnovationsArtificial Intelligence Applications

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

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Woschank, Manuel, Rauch, Erwin, Zsifkovits, Helmut·2020·Sustainability
DOI 10.3390/su12093760
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Abstract

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

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  • AI, ML, and DL are all important technologies in the field of robotics.

    “Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are all important technologies in the field of robotics [1] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [1]

    Section 4.1 identifies AI, ML, and DL as technologies for improving logistics processes and includes swarm robotics as a smart-warehouse application. The paper does not establish the broader claim that all three are important technologies in robotics generally; its discussion is bounded to smart logistics. No page number is available in our copy. (LLM, conf=0.82)

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Research works printed but not linked: 62

  1. Matt, D.T.; Modrák, V.; Zsifkovits, H. Industry 4.0 for SMEs; Springer: Berlin/Heidelberg, Germany, 2020.
  2. Zsifkovits, H.; Woschank, M. Smart Logistics-Technologiekonzepte und Potentiale. BHM Berg Hüttenmännische Monatshefte 2019, 164, 42-45. [CrossRef] · DOI
  3. Dallasega, P.; Woschank, M.; Ramingwong, S.; Tippayawong, K.; Chonsawat, N. Field study to identify requirements for smart logistics of European, US and Asian SMEs. In Proceedings of the International Conference on Industrial Engineering and Operations Management, Bangkok, Thailand, 5-7 March 2019; pp. 844-855.
  4. Cioffi, R.; Travaglioni, M.; Piscitelli, G.; Petrillo, A.; de Felice, F. Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions. Sustainability 2020, 12, 1-24. [CrossRef] · DOI · Paper page
  5. Mc Carthy, J.; Minsky, M.L.; Rochester, N.; Shannon, C.E. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. AI Mag. 2006, 27, 12-14.
  6. Shalev-Shwartz, S.; Ben-David, S. Understanding Machine Learning: From Theory to Algorithms; Cambridge University Press: Cambridge, UK, 2014.
  7. Diez-Olivan, A.; Del Ser, J.; Galar, D.; Sierra, B. Data Fusion and Machine Learning for Industrial Prognosis: Trends and Perspectives towards Industry 4.0. Inf. Fusion 2019, 50, 92-111. [CrossRef] · DOI · Paper page
  8. Dey, N.; Ashour, A.S.; Nguyen, G.N. Deep Learning for Multimedia Content Analysis. In Mining Multimedia Documents; Karaa, W.B.A., Dey, N., Eds.; CRC Press: Boca Raton, FL, USA, 2017; pp. 193-203.
  9. Hamet, P.; Tremblay, J. Artificial intelligence in medicine. Metab. Clin. Exp. 2017, 69S, 36-40. [CrossRef] · DOI · Paper page
  10. Baryannis, G.; Validi, S.; Dani, S.; Antoniou, G. Supply chain risk management and artificial intelligence: State of the art and future research directions. Int. J. Prod. Res. 2018, 57, 2179-2202. [CrossRef] · DOI
  11. Hokka, M.; Kaakinen, P.; Polkki, T. A systematic review: Non-pharmacological interventions in treating pain in patients with advanced cancer. J. Adv. Nurs. 2014, 70, 1954-1969. [CrossRef] · DOI
  12. Töpfer, A. Erfolgreich Forschen: Ein Leitfaden für Bachelor-, Master-Studierende und Doktoranden, 3rd ed.; Springer: Berlin/Heidelberg, Germany, 2012.
  13. Booth, A.; Sutton, A.; Papaioannou, D. Systematic Approaches to a Successful Literature Review, 2nd ed.; SAGE: London, UK, 2016.
  14. Ali, N.B.; Usman, M. Reliability of search in systematic reviews: Towards a quality assessment framework for the automated-search strategy. Inf. Softw. Technol. 2018, 99, 133-147. [CrossRef] · DOI
  15. Palmarini, R.; Erkoyuncu, J.A.; Roy, R.; Torabmostaedi, H. A systematic review of augmented reality applications in maintenance. Robot. Comput. Manuf. 2018, 49, 215-228. [CrossRef] · DOI
  16. Tranfield, D.; Denyer, D.; Smart, P. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. Br. J. Manag. 2003, 14, 207-222. [CrossRef] · DOI · Paper page
  17. Denyer, D.; Tranfield, D. Producing a systematic review. In The Sage Handbook of Organizational Research Methods;
  18. Durach, C.F.; Kembro, J.; Wieland, A. A New Paradigm for Systematic Literature Reviews in Supply Chain Management. J. Supply Chain Manag. 2017, 53, 67-85. [CrossRef] · DOI
  19. Gursch, H.; Wuttei, A.; Gangloff, T. Learning Systems for Manufacturing Management Support. 2016. Available online: http://ceur-ws.org/Vol-1793/paper5.pdf (accessed on 1 March 2020).
  20. Brodsky, A.; Krishnamoorthy, M.; Menasce, D.A.; Shao, G.; Rachuri, S. Toward smart manufacturing using decision analytics. In Proceedings of the 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 27-30 October 2014; pp. 967-977.
  21. Qu, S.; Jian, R.; Chu, T.; Wang, J.; Tan, T. Computational reasoning and learning for smart manufacturing under realistic conditions. In Proceedings of the 2014 International Conference on Behavior, Economic and Social Computing (BESC), Shanghai, China, 30 October-2 November 2014; pp. 1-8.
  22. Bonino, D.; Vergori, P. Agent Marketplaces and Deep Learning in Enterprises: The COMPOSITION Project. In Proceedings of the 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), Turin, Italy, 4-8 July 2017; pp. 749-754.
  23. Peres, R.S.; Dionisio Rocha, A.; Leitao, P.; Barata, J. IDARTS-Towards intelligent data analysis and real-time supervision for industry 4.0. Comput. Ind. 2018, 101, 138-146. [CrossRef] · DOI
  24. Guo, Q.; Miyamae, Y.; Wang, Z.; Taniuchi, K.; Yang, H.; Liu, Y. Senvis-Net: Learning from Imbalanced Machinery Data by Transferring Visual Element Detectors. Int. J. Mach. Learn. Comput. 2018, 8, 416-422.
  25. Ferrer, B.R.; Mohammed, W.M.; Martinez Lastra, J.L.; Villalonga, A.; Beruvides, G.; Castano, F.; Haber, R.E. Towards the Adoption of Cyber-Physical Systems of Systems Paradigm in Smart Manufacturing Environments. In Proceedings of the 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), Porto, Portugal, 18-20 July 2018; pp. 792-799.
  26. Thalmann, S.; Mangler, J.; Schreck, T.; Huemer, C.; Streit, M.; Pauker, F.; Weichhart, G.; Schulte, S.; Kittl, C.; Pollak, C.; et al. Data Analytics for Industrial Process Improvement A Vision Paper. In Proceedings of the 2018 IEEE 20th Conference on Business Informatics (CBI), Vienna, Austria, 11-13 July 2018; pp. 92-96.
  27. Morozov, D.; Lezoche, M.; Panetto, H. Multi-paradigm modelling of Cyber-Physical Systems. IFAC-PapersOnLine 2018, 51, 1385-1390. [CrossRef] · DOI
  28. Marrella, A.; Mecella, M. Cognitive Business Process Management for Adaptive Cyber-Physical Processes. In Business Process Management Workshops;
  29. Marrella, A.; Mecella, M.; Sardiña, S. Supporting adaptiveness of cyber-physical processes through action-based formalisms. AI Commun. 2018, 31, 47-74. [CrossRef] · DOI
  30. Subakti, H.; Jiang, J.-R. Indoor Augmented Reality Using Deep Learning for Industry 4.0 Smart Factories. In Proceedings of the 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan, 23-27 July 2018; pp. 63-68.
  31. Susto, G.A.; Schirru, A.; Pampuri, S.; Beghi, A.; De Nicolao, G. A hidden-Gamma model-based filtering and prediction approach for monotonic health factors in manufacturing. Control. Eng. Pract. 2018, 74, 84-94. [CrossRef] · DOI
  32. Wang, K., Wang, Y., Strandhagen, J.O., Yu, T., Eds.; Springer: Berlin/Heidelberg, Germany, 2018; pp. 1-9.
  33. Klein, P.; Bergmann, R. Data Generation with a Physical Model to Support Machine Learning Research for Predictive Maintenance. In Proceedings of the LWDA 2018, Mannheim, Germany, 22-24 August 2018; pp. 179-190.
  34. Cho, S.; May, G.; Tourkogiorgis, I.; Perez, R.; Lazaro, O.; de La Maza, B.; Kiritsis, D. A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future. In Advances in Production Management Systems. Smart Manufacturing for Industry 4.0; Moon, I., Lee, G.M., Park, J., Kiritsis, D., von Cieminski, G., Eds.; Springer: Berlin/Heidelberg, Germany, 2018; pp. 311-317.
  35. Wuest, T.; Weimer, D.; Irgens, C.; Thoben, K.-D. Machine learning in manufacturing: Advantages, challenges, and applications. Prod. Manuf. Res. 2016, 4, 23-45. [CrossRef] · DOI · Paper page
  36. Terziyan, V.; Gryshko, S.; Golovianko, M. Patented intelligence: Cloning human decision models for Industry 4.0. J. Manuf. Syst. 2018, 48, 204-217. [CrossRef] · DOI
  37. Venkatapathy, A.K.R.; Bayhan, H.; Zeidler, F.; Hompel, M. Human Machine Synergies in Intra-Logistics: Creating a Hybrid Network for Research and Technologies. In Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, Prague, Czech Republic, 3-6 September 2017; pp. 1065-1068.
  38. Zeidler, F.; Bayhan, H.; Ramachandran Venkatapathy, A.K.; ten Hompel, M. Referenzfeld zur Erforschung und Entwicklung neuartiger hybrider Formen der Zusammenarbeit von Menschen und Maschinen in der Logistik. Logist. J. Proc. 2017, 1, 1-8. Available online: https://www.logistics-journal.de/proceedings/2017/ 4593/zeidler_2017.pdf (accessed on 1 March 2020). [CrossRef] · DOI
  39. Lolli, F.; Balugani, E.; Ishizaka, A.; Gamberini, R.; Rimini, B.; Regattieri, A. Machine learning for multi-criteria inventory classification applied to intermittent demand. Prod. Plan. Control. 2018, 30, 76-89. [CrossRef] · DOI
  40. He, L.; Li, W.; Zhang, Y.; Cao, J. Review of Swarm Intelligence Algorithms for Multi-objective Flowshop Scheduling. In Internet and Distributed Computing Systems;
  41. Xiang, Y., Sun, J., Fortino, G., Guerrieri, A., Jung, J.J., Eds.; Springer: Berlin/Heidelberg, Germany, 2018; pp. 258-269.
  42. Zhang, J.; Ding, G.; Zou, Y.; Qin, S.; Fu, J. Review of job shop scheduling research and its new perspectives under Industry 4.0. J. Intell. Manuf. 2017, 30, 1809-1830. [CrossRef] · DOI
  43. Madureira, A.M., Abraham, A., Gamboa, D., Novais, P., Eds.; Springer: Berlin/Heidelberg, Germany, 2017; pp. 984-994.
  44. Luetkehoff, B.; Blum, M.; Schroeter, M. Self-learning Production Control Using Algorithms of Artificial Intelligence. In Collaboration in a Data-Rich World; Camarinha-Matos, L.M., Afsarmanesh, H., Fornasiero, R., Eds.; Springer: Berlin/Heidelberg, Germany, 2017; pp. 299-306.
  45. Wen, J.; He, L.; Zhu, F. Swarm Robotics Control and Communications: Imminent Challenges for Next Generation Smart Logistics. IEEE Commun. Mag. 2018, 56, 102-107. [CrossRef] · DOI
  46. Laux, H.; Bytyn, A.; Ascheid, G.; Schmeink, A.; Kurt, G.K.; Dartmann, G. Learning-based indoor localization for industrial applications. In Proceedings of the 15th ACM International Conference, New York, NY, USA, 24-29 September 2018; pp. 355-362.
  47. Ademujimi, T.T.; Brundage, M.P.; Prabhu, V.V. A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis. In Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing; Lödding, H., Riedel, R., Thoben, K.-D., von Cieminski, G., Kiritsis, D., Eds.; Springer: Berlin/Heidelberg, Germany, 2017; pp. 407-415.
  48. Teschemacher, U.; Reinhart, G. Ant Colony Optimization Algorithms to Enable Dynamic Milkrun Logistics. Procedia CIRP 2017, 63, 762-767. [CrossRef] · DOI
  49. Li, D.; Deng, L.; Cai, Z.; Franks, B.; Yao, X. Intelligent Transportation System in Macao Based on Deep Self-Coding Learning. IEEE Trans. Ind. Inform. 2018, 14, 3253-3260. [CrossRef] · DOI
  50. Cheng, J.; Wu, W.; Cao, J.; Li, K. Fuzzy group-based intersection control via vehicular networks for smart transportations. IEEE Trans. Ind. Inform. 2017, 13, 751-758. [CrossRef] · DOI
  51. Li, B.Y.S.; Yeung, L.F.; Tsang, K.F. Analysing traffic condition based on IoT technique. In Proceedings of the 2014 IEEE International Conference on Consumer Electronics (ICCE-C 2015), Shenzhen, China, 10-13 January 2015; pp. 1-4.
  52. Edelstein, R. Smarter transportation management through its. In Proceedings of the 21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World, Detroit, MI, USA, 7-11 September 2014; pp. 1-4.
  53. Woschank, M. The Impact of Decision Making Maturity on Decision Making Efficiency. Ph.D. Thesis, University of Latvia, Riga, Latvia, 2018.
  54. Rai, A. Explainable AI: From black box to glass box. J. Acad. Mark. Sci. 2019, 48, 137-141. [CrossRef] · DOI · Paper page
  55. Aytug, H.; Bhattacharyya, S.; Koehler, G.; Snowdon, J. A review of machine learning in scheduling. IEEE Trans. Eng. Manag. 1994, 41, 165-171. [CrossRef] · DOI
  56. Miehe, R.; Bauernhansl, T.; Schwarz, O.; Traube, A.; Lorenzoni, A.; Waltersmann, L.; Full, J.; Horbelt, J.; Sauer, A. The biological transformation of the manufacturing industry-envisioning biointelligent value adding. Procedia CIRP 2018, 72, 739-743. [CrossRef] · DOI
  57. Matt, D.T.; Riedl, M.; Rauch, E. Die Natur als Inspiration. ZWF Zeitschrift Wirtschaftlichen Fabrikbetrieb 2020, 115, 158-161. [CrossRef] · DOI
  58. Giusti, R.; Manerba, D.; Bruno, G.; Tadei, R. Synchromodal logistics: An overview of critical success factors, enabling technologies, and open research issues. Transp. Res. Part. E Logist. Transp. Rev. 2019, 129, 92-110. [CrossRef] · DOI
  59. Le, L.T.; Lee, G.; Park, K.S.; Kim, H. Neural network-based fuel consumption estimation for container ships in Korea. Marit. Policy Manag. 2020, 1, 1-18. [CrossRef] · DOI
  60. Liu, C.; Park, E.; Jiang, F. Examining effects of context-awareness on ambient intelligence of logistics service quality: User awareness compatibility as a moderator. J. Ambient. Intell. Humaniz. Comput. 2018, 11, 1413-1420. [CrossRef] · DOI
  61. Ceyhun, G.Ç. Recent Developments of Artificial Intelligence in Business Logistics: A Maritime Industry Case. In Digital Business Strategies in Blockchain Ecosystems; Springer: Berlin/Heidelberg, Germany, 2020; pp. 343-353.
  62. Karballaeezadeh, N.; Zaremotekhases, F.; Shamshirband, S.; Mosavi, A.; Nabipour, N.; Csiba, P.; Várkonyi-Kóczy, A.R. Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems. Energies 2020, 13, 1718-1741. [CrossRef] · DOI

Cited by

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Authors
Woschank, M., Rauch, E., & Zsifkovits, H.
Affiliations
  • Montanuniversität Leoben (AT)
  • Free University of Bozen-Bolzano (IT)
Corresponding author
Woschank, Manuel
ORCID
Venue
Sustainability
Publisher
MDPI AG
Published
2020-05-06
Volume
12
Issue
9
Pages
3760
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su12093760
OpenAlex
W3022135700
Cited by
338
References
3
Citations by year
2026: 34 · 2025: 71 · 2024: 95 · 2023: 54 · 2022: 47 · 2021: 33 · 2020: 4
FWCI
21.96
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Aaltodoc (Aalto University) (submittedVersion, cc-by-nc-nd)
  • Open Research (University of Surrey) (submittedVersion, cc-by)
  • INFM-OAR (INFN Catania) (submittedVersion, cc-by)
Funders
European Commission (HORIZON2020, 734713)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Transport and Logistics Innovations
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence, machine learning, deep learning, smart logistics, Industry 4.0, smart manufacturing, logistics management, industrial enterprises, digitalization, automation, conceptual framework

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