Using Analytic Hierarchical Process for Scheduling Problems Based on Smart Lots and Their Quality Prediction Capability

  • Emmanuel ZimmermannEmail author
  • Hind Bril El-Haouzi
  • Philippe Thomas
  • Rémi Pannequin
  • Mélanie Noyel
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 803)


The scheduling problem in manufactories with high rework rates remains an actual complex research source. This paper presents a combination of a predictive schedule with proactive decision making based on smart lots. Each batch embeds an algorithm which allows predicting the risk of rework on the next workstation. If the risk of rework is above a defined threshold, a collaborative re-scheduling decision, using analytic hierarchical process (AHP), is initiated for the other batches. A simulation model, inspired from a lacquering robot case study is described. Then, the results of different scenarios are presented and discussed.


Proactive decision making Analytic Hierarchical Process (AHP) Quality prediction Smart lots 


  1. 1.
    Hanssmann, F., Hess, S.W.: A linear programming approach to production and employment scheduling. Manag. Technol. 1(1), 46–51 (1960)MathSciNetGoogle Scholar
  2. 2.
    Held, M., Karp, R.M.: A dynamic programming approach to sequencing problems. J. Soc. Ind. Appl. Math. 10(1), 196–210 (1962)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Zimmermann, E., El Haouzi, H.B., Thomas, P., Thomas, A., Noyel, M.: A hybrid manufacturing control based on smart lots in a disrupted industrial context. In: Proceedings of 20th IFAC World Congress, IFAC 2017 (2017)Google Scholar
  4. 4.
    Noyel, M., Thomas, P., Thomas, A., Charpentier, P.: Reconfiguration process for neuronal classification models: application to a quality monitoring problem. Comput. Ind. 83, 78–91 (2016)CrossRefGoogle Scholar
  5. 5.
    Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95(1), 215–233 (2007)CrossRefGoogle Scholar
  6. 6.
    Pitt, J., Kamara, L., Sergot, M., Artikis, E.: Voting in multi-agent systems. Comput. J. (2006). Scholar
  7. 7.
    Schmickl, T., et al.: Get in touch: cooperative decision making based on robot-to-robot collisions. Auton. Agents Multi-Agent Syst. 18(1), 133–155 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Xiang, W., Lee, H.P.: Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Eng. Appl. Artif. Intell. 21(1), 73–85 (2008)CrossRefGoogle Scholar
  9. 9.
    Teodorovic, D.: Transport modeling by multi-agent systems: a swarm intelligence approach. J. Transp. Plan. Technol. 26(4), 289–312 (2003)CrossRefGoogle Scholar
  10. 10.
    Parsons, S., Wooldridge, M.: Game theory and decision theory in multi-agent systems. Auton. Agents Multi-Agents Syst. 5, 243 (2002). Scholar
  11. 11.
    Saaty, T.L., Vargas, L.G.: Hierarchical analysis of behavior in competition: prediction in chess. Behav. Sci. 25(3), 180–191 (1980)CrossRefGoogle Scholar
  12. 12.
    Chan, F., Chung, S., Wadhwa, S.: A hybrid genetic algorithm for production and distribution. Omega 33(4), 345–355 (2005)CrossRefGoogle Scholar
  13. 13.
    Azadeh, A., Ghaderi, S.F., Izadbakhsh, H.: Integration of DEA and AHP with computer simulation for railway system improvement and optimization. Appl. Math. Comput. 195(2), 775–785 (2008)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Momoh, J.A., Zhu, J.: Optimal generation scheduling based on AHP/ANP. IEEE Trans. Syst. Man Cybern. Part B Cybern. 33(3), 531–535 (2003)CrossRefGoogle Scholar
  15. 15.
    Analytic Hierarchy Process AHP Tutorial. Accessed 05 Apr 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Emmanuel Zimmermann
    • 1
    • 2
    Email author
  • Hind Bril El-Haouzi
    • 1
  • Philippe Thomas
    • 1
  • Rémi Pannequin
    • 1
  • Mélanie Noyel
    • 2
  1. 1.Université de Lorraine, CRAN, UMR 7039Vandœuvre-lès-NancyFrance
  2. 2.Acta-Mobilier, Parc d’activité Macherin Auxerre NordMonéteauFrance

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