Abstract
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.
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Hanssmann, F., Hess, S.W.: A linear programming approach to production and employment scheduling. Manag. Technol. 1(1), 46–51 (1960)
Held, M., Karp, R.M.: A dynamic programming approach to sequencing problems. J. Soc. Ind. Appl. Math. 10(1), 196–210 (1962)
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)
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)
Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95(1), 215–233 (2007)
Pitt, J., Kamara, L., Sergot, M., Artikis, E.: Voting in multi-agent systems. Comput. J. (2006). https://doi.org/10.1093/comjnl/bxh164
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)
Xiang, W., Lee, H.P.: Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Eng. Appl. Artif. Intell. 21(1), 73–85 (2008)
Teodorovic, D.: Transport modeling by multi-agent systems: a swarm intelligence approach. J. Transp. Plan. Technol. 26(4), 289–312 (2003)
Parsons, S., Wooldridge, M.: Game theory and decision theory in multi-agent systems. Auton. Agents Multi-Agents Syst. 5, 243 (2002). https://doi.org/10.1023/A:1015575522401
Saaty, T.L., Vargas, L.G.: Hierarchical analysis of behavior in competition: prediction in chess. Behav. Sci. 25(3), 180–191 (1980)
Chan, F., Chung, S., Wadhwa, S.: A hybrid genetic algorithm for production and distribution. Omega 33(4), 345–355 (2005)
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)
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)
Analytic Hierarchy Process AHP Tutorial. http://people.revoledu.com/kardi/tutorial/AHP/AHP.htm. Accessed 05 Apr 2018
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Zimmermann, E., El-Haouzi, H.B., Thomas, P., Pannequin, R., Noyel, M. (2019). Using Analytic Hierarchical Process for Scheduling Problems Based on Smart Lots and Their Quality Prediction Capability. In: Borangiu, T., Trentesaux, D., Thomas, A., Cavalieri, S. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing. SOHOMA 2018. Studies in Computational Intelligence, vol 803. Springer, Cham. https://doi.org/10.1007/978-3-030-03003-2_26
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DOI: https://doi.org/10.1007/978-3-030-03003-2_26
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