Intelligence Amplification Framework for Enhancing Scheduling Processes

  • Andrej DobrkovicEmail author
  • Luyao Liu
  • Maria-Eugenia Iacob
  • Jos van Hillegersberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10022)


The scheduling process in a typical business environment consists of predominantly repetitive tasks that have to be completed in limited time and often containing some form of uncertainty. The intelligence amplification is a symbiotic relationship between a human and an intelligent agent. This partnership is organized to emphasize the strength of both entities, with the human taking the central role of the objective setter and supervisor, and the machine focusing on executing the repetitive tasks. The output efficiency and effectiveness increase as each partner can focus on its native tasks. We propose the intelligence amplification framework that is applicable in typical scheduling problems encountered in the business domain. Using this framework we build an artifact to enhance scheduling processes in synchromodal logistics, showing that a symbiotic decision maker performs better in terms of efficiency and effectiveness.


Intelligence amplification Intelligent agents Synchromodal logistics Scheduling 


  1. 1.
    Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24, 45–77 (2007)CrossRefGoogle Scholar
  2. 2.
    Licklider, J.C.: Man-computer symbiosis. In: IRE Transactions on Human Factors Electron, pp. 4–11 (1960)Google Scholar
  3. 3.
    Griffith, D., Greitzer, F.L.: Neo-symbiosis: the next stage in the evolution of human information interaction. Int. J. Cogn. Inf. Nat. Intell. (IJCINI) 1, 39–52 (2007)CrossRefGoogle Scholar
  4. 4.
    Williams, D.P., Couillard, M., Dugelay, S.: On human perception and automatic target recognition: strategies for human-computer cooperation. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 4690–4695 (2014)Google Scholar
  5. 5.
    Garcia, A.C.B.: AGUIA: agents guidance for intelligence amplification in goal oriented tasks. In: 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), pp. 338–344 (2010)Google Scholar
  6. 6.
    Casini, E., Depree, J., Suri, N., Bradshaw, J.M., Nieten, T.: Enhancing decision-making by leveraging human intervention in large-scale sensor networks. In: 2015 IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), pp. 200–205 (2015)Google Scholar
  7. 7.
    Woolley, B.G., Stanley, K.O.: A novel human-computer collaboration: combining novelty search with interactive evolution. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, pp. 233–240 (2014)Google Scholar
  8. 8.
    Delibasic, B., Vukicevic, M., Jovanovic, M.: White-box decision tree algorithms: a pilot study on perceived usefulness, perceived ease of use, and perceived understanding. Int. J. Eng. Edu. 293, 674–687 (2013)Google Scholar
  9. 9.
    Ahmed, A.-I., Hasan, M.M.: A hybrid approach for decision making to detect breast cancer using data mining and autonomous agent based on human agent teamwork. In: 2014 17th International Conference on Computer and Information Technology (ICCIT), pp. 320–325 (2014)Google Scholar
  10. 10.
    Mes, M.R., Iacob, M.E.: Synchromodal transport planning at a logistics service provider. In: Zijm, H., Klumpp, M., Clausen, U., ten Hompel, M. (eds.) Logistics and Supply Chain Innovation, pp. 23–36. Springer, Heidelberg (2016)CrossRefGoogle Scholar
  11. 11.
    Singh, P.M.: Developing a service oriented IT platform for synchromodal transportation. In: On the Move to Meaning Ful Inrternet Systems OTM 2014 Workshop, pp. 30–36 (2016)Google Scholar
  12. 12.
    Dobrkovic, A., Iacob, M.-E., van Hillegersberg, J., Mes, M., Glandrup, M.: Towards an approach for long term AIS-based prediction of vessel arrival times. In: ten Hompel, M., Clausen, U., Klumpp, M., Zijm, H. (eds.) Logistics and Supply Chain Innovation, pp. 281–294. Springer, Cham (2016)CrossRefGoogle Scholar
  13. 13.
    Buiel, E., Visschedijk, G., Lebesque, L., Lucassen, I., Riessen, B.v., Rijn, A.v., et al.: Synchro mania-design and evaluation of a serious game creating a mind shift in transport planning, In: 46th International Simulation and Gaming Association Conference, ISAGA 2015, Kyoto, Japan, 18–25 July 2015, pp. 1–12 (2015)Google Scholar
  14. 14.
    Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5, 32–38 (1957)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Annett, J.: Hierarchical task analysis. Handbook Cogn. Task Des. 2, 17–35 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Andrej Dobrkovic
    • 1
    Email author
  • Luyao Liu
    • 1
  • Maria-Eugenia Iacob
    • 1
  • Jos van Hillegersberg
    • 1
  1. 1.Industrial Engineering and Business Information SystemsUniversity of TwenteEnschedeThe Netherlands

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