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An Improved Production Planning Approach Under the Consideration of Production Order Interdependencies

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Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future (SOHOMA 2019)

Abstract

Commonly, methods applied in production planning may lead to production orders flowing across similar sequences of machines within similar periods of time. However, within such spatiotemporal neighbourhoods, interdependency effects among production orders may arise causing compounding delay among those production orders. We provide a heuristic for improving production plan methods, such that compounding delays as a result of interdependency effects can be mitigated. Through this heuristic we are able to improve the predictability of logistics performance indicators of production orders and hence improve the reliability of production order master data as a central input to production planning.

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References

  1. Das, B., Rickard, J., Shah, N., Macchietto, S.: An investigation on integration of aggregate production planning, master production scheduling and short-term production scheudling of batch process operations through a common data model. Comput. Chem. Eng. 24(2–7), 1625–1631 (2000)

    Article  Google Scholar 

  2. Kopanos, G.M., Puigjaner, L., Maravelias, C.T.: Production planning and scheduling of parallel continuous processes with product families. Ind. Eng. Chem. Res. [Internet] 50(3), 1369–1378 (2011). http://www.scopus.com/inward/record.url?eid=2-s2.0-79952804936&partnerID=tZOtx3y1

    Article  Google Scholar 

  3. Hopp, W.J., Spearman, M.L.: Factory Physics, 3rd edn. McGraw-Hill Higher Education, New York (2008)

    Google Scholar 

  4. Xie, J., Zhao, X., Lee, T.S.: Freezing the master production schedule under single resource constraint and demand uncertainty. Int. J. Prod. Econ. 83, 65–84 (2003)

    Article  Google Scholar 

  5. Maravelias, C.T., Sung, C.: Integration of production planning and scheduling: overview, challenges and opportunities. Comput. Chem. Eng. 33(12), 1919–1930 (2009)

    Article  Google Scholar 

  6. Auer, S., Tutsch, H., Sihn, W.: Classification of interdependent planning restrictions and their various impacts on long-, mid- and short term planning of high variety production. In: 44th CIRP International Conference on Manufacturing Systems (2011)

    Google Scholar 

  7. Matyas, K., Auer, S.: Combination of planning methods in a comprehensive production planning approach for sequenced production lines. CIRP Ann. Manuf. Technol. [Internet] 61(1), 445–448 (2012). https://doi.org/10.1016/j.cirp.2012.03.076

    Article  Google Scholar 

  8. Li, Z., Ierapetritou, M.G.: Production planning and scheduling integration through augmented Lagrangian optimization. Comput. Chem. Eng. [Internet] 34(6), 996–1006 (2010). https://doi.org/10.1016/j.compchemeng.2009.11.016

    Article  Google Scholar 

  9. Józefowska, J., Zimniak, A.: Optimization tool for short-term production planning and scheduling. Int. J. Prod. Econ. 112(1), 109–120 (2008)

    Article  Google Scholar 

  10. Jiang, X., Guo, L., Hong, S., Zhou, J.: Modelling delay propagation within a train communication network. In: 2015 Annual Reliability and Maintainability Symposium (RAMS) [Internet]. IEEE pp. 1–6 (2015). http://ieeexplore.ieee.org/document/7105059/. Accessed 9 Oct 2018

  11. Hwang, C., Liu, J.-R.: A simulation model for estimating knock-on delay of taiwan regional railway. J. East Asia Soc. Transp. Stud. 8, 1110–1125 (2010)

    Google Scholar 

  12. Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)

    Article  Google Scholar 

  13. Van der Weele, K., Spit, W., Mekkes, T., Van der Meer, D.: From granular flux model to traffic flow description. In: Traffic and Granular Flow 2003, pp. 569–577. Springer, Heidelberg (2005)

    Google Scholar 

  14. Huang, R.H., Yang, C.L.: Solving a multi-objective overlapping flow-shop scheduling. Int. J. Adv. Manuf. Technol. 42(9–10), 955–962 (2009)

    Article  Google Scholar 

  15. Moon, H., Kim, H., Kim, C., Kang, L.: Development of a schedule-workspace interference management system simultaneously considering the overlap level of parallel schedules and workspaces. Autom. Constr. 39, 93–105 (2014)

    Article  Google Scholar 

  16. Backus, P., Janakiram, M., Mowzoon, S., Runger, G.C., Bhargava, A.: Mining approach factory cycle-time prediction with a data-mining approach. IEEE Trans. Semicond. Manuf. 19(2), 252–258 (2006)

    Article  Google Scholar 

  17. Öztürk, A., Kayaligil, S., Özdemirel, N.E.: Manufacturing lead time estimation using data mining. Eur. J. Oper. Res. 173, 683–700 (2006)

    Article  MathSciNet  Google Scholar 

  18. Azadeh, A., Ziaeifar, A.: An inteligent algorithm for optimum forecasting of manfuacturing lead times in fuzzy and crisp environments. Int. J. Logist. Syst. Manag. 16(2), 186–210 (2013)

    Article  Google Scholar 

  19. Windt, K., Hütt, M.-T.: Exploring due date reliability in production systems using data mining methods adapted from gene expression analysis. CIRP Ann. Manuf. Technol. 60(1), 473–476 (2011)

    Article  Google Scholar 

  20. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., et al.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide. Proc. Natl. Acad. Sci. 102(43), 15545–15550 (2005)

    Article  Google Scholar 

  21. Rajendran, C., Holthaus, O.: A comparative study of dispatching rules in dynamic flowshops and jobshops. Eur. J. Oper. Res. 116(1), 156–170 (1999)

    Article  Google Scholar 

  22. Ruben, R., Mahmoodi, F.: Lead time prediction in unbalanced production systems. Int. J. Prod. Res. 38(7), 1711–1729 (2000)

    Article  Google Scholar 

  23. Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of Eighteenth Annual ACM-SIAM Symposium Discrete Algorithms, vol. 8, pp. 1027–1025 (2007)

    Google Scholar 

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Correspondence to Victor Vican .

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Bendul, J., Vican, V., Hütt, MT. (2020). An Improved Production Planning Approach Under the Consideration of Production Order Interdependencies. In: Borangiu, T., Trentesaux, D., Leitão, P., Giret Boggino, A., Botti, V. (eds) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2019. Studies in Computational Intelligence, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-030-27477-1_18

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