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A Method to Improve Planning of Product Placement on a Printing Sheet

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Języki publikacji
EN
Abstrakty
EN
Major manufactures are moving towards a sustainability goal. This paper introduces the results of collaboration with the leading company in the packaging and advertising industry in Germany and Poland. The problem addresses the manufacturing planning problem in terms of minimizing the total cost of production. The challenge was to bring a new production planning method into cardboard manufacturing and paper processing which minimizes waste, improves the return of expenses, and automates daily processes heavily dependent on the production planners’ experience. The authors developed a module that minimizes the total cost, which reduces the overproduction and is used by the company’s manufacturing planning team. The proposed approach incorporates planning allowances rules to compromise the manufacturing requirements and production cost minimization.
Twórcy
  • Wroclaw University of Economics and Business, Wroclaw, Poland
  • Poznan University of Technology, Poznan, Poland
  • Poznan University of Technology, Poznan, Poland
Bibliografia
  • Alonso-Pecina F., and Romero D.A (2018). Hybrid Simulated Annealing/Linear Programming Approach for the Cover Printing Problem, Math. Probl. Eng., vol. 3, pp. 1–11.
  • Ashayeri J. and Selen W. (2005). An application of a unified capacity planning system, Int. J. Oper. Prod. Manag., vol. 25, no. 9, pp. 917–937.
  • Belhadi A., Zkik K., Cherrafi A., Yusof S.M., and El fezazi S. (2019). Understanding Big Data Analytics for Manufacturing Processes: Insights from Literature Review and Multiple Case Studies, Comput. Ind. Eng., vol. 137, pp. 1–18.
  • Bilgen B. and Çelebi Y. (2013). Integrated production scheduling and distribution planning in dairy supply chain by hybrid modeling, Ann. Oper. Res., vol. 211, no. 1, pp. 55–82.
  • Bonney M. (2000). Reflections on production planning and control (PPC), Gestão&Produção, vol. 7, no. 3, pp. 181–207.
  • Bueno A., Godinho Filho M., and Frank A.G. (2020). Smart production planning and control in the Industry 4.0 context: A systematic literature review, Comput. Ind. Eng., vol. 149, pp. 1–21.
  • Burger A.P., Jacobs C.G., and van Vuuren J.H., Visagie S.E. (2015). Scheduling multi-colour print jobs with sequence-dependent setup times, J. Sched., vol. 18, no. 2, pp. 131–145.
  • Chergui A., Hadj-Hamou K., and Vignat F. (2018). Production scheduling and nesting in additive manufacturing, Comput. Ind. Eng., vol. 126, pp. 292–301.
  • De Antón J., Senovilla J., Varona J.M., and Acebes F. (2020). Production planning in 3D printing factories, International Journal of Production Management and Engineering, vol. 8, no. 75, pp. 75–86.
  • Dvorak F., Micali M., and Mathieu M. (2018). Planning and scheduling in additive manufacturing, Intel. Artif., vol. 21, no. 62, pp. 40–52.
  • Ekici A., Ergun Ö., Keskinocak P., and Lagoudakis M.G. (2010). Optimal job splitting on a multi-slot machine with applications in the printing industry, Nav. Res. Log., vol. 57, no. 3, pp. 237–251.
  • Elaoud S., Teghem J., and Bouaziz B. (2007). Genetic algorithms to solve the cover printing problem, Comput. Oper. Res., vol. 34, no. 11, pp. 3346–3361.
  • Fazel Zarandi M.H., Sadat Asl A.A., Sotudian S., and Castillo O. (2020). A state of the art review of intelligent scheduling, Artif. Intell. Rev., vol. 53, no. 1, pp. 501–593.
  • Frits M. and Bertok B. (2020). Scheduling custom printed napkin manufacturing by P-graphs, Comput. Chem. Eng., vol. 141, pp. 1–19.
  • Hsieh Y.C. and You P.S. (2014), An Immune Evolutionary Approach for the Label Printing Problem, Int. J. Comput. Int. Sys., vol. 7, no. 3, pp. 515–523.
  • Jeng S.Y., Lin C.W., Tseng M.L., and Jantarakolica T. (2019). Cradle-to-cradle zero discharge production planning system for the pulp and paper industry using a fuzzy hybrid optimization model, Management of Environmental Quality: An International Journal, vol. 31, no. 3, pp. 645–663.
  • Karagul H.F., Warsing Jr. D.P., Hodgson T.J., Kapadia M.S., and Uzsoy R. (2018). A comparison of mixed integer programming formulations of the capacitated lot-sizing problem, Int. J. Prod. Res., vol. 56, no. 23, pp. 7064-7084.
  • Koh S.C.L. and Simpson M. (2007). Could enterprise resource planning create a competitive advantage for small businesses?, Benchmarking, vol. 14, no. 1, pp. 59–76.
  • Kucukkoc I., Li Q., and Zhang D.Z. (2016). Increasing the utilisation of additive manufacturing and 3D printing machines considering order delivery times, 19th International Working Seminar on Production Economics, pp. 195–201. Retrieved from https://www.researchgate.net/publication/318373931_Increasing_the_utilisation_of_additive_manufacturing_and_3D_printing_machines_considering_order_delivery_times.
  • Kusiak, A. (2018). Smart manufacturing, Int. J. Prod. Res., vol. 56, no. 1–2, pp. 508–517.
  • Li Q., Kucukkoc I., and Zhang D.Z. (2017). Production planning in additive manufacturing and 3D printing, Comput. Oper. Res., vol. 83, pp. 1339–1351.
  • Mohan S.R., Neogy S.K., Seth A., Garg N.K., and Mittal S. (2007). An optimization model to determine master designs and runs for advertisement printing, Journal of Mathematical Modelling and Algorithms, vol. 6, no. 2, pp. 259–271.
  • Oluyisola O.E., Sgarbossa F., and Strandhagen J.O. (2020). Smart production planning and control: Concept, use-cases and sustainability implications, Sustainability (Switzerland), vol. 12, no. 9.
  • Oztemel E. and Gursev S. (2020). Literature review of Industry 4.0 and related technologies, J. Intell. Manuf., Springer, vol. 31, pp. 127–182.
  • Romero D. and Alonso-Pecina F. (2012). Ad hoc heuristic for the cover printing problem, Discrete Optim., vol. 9, no. 1, pp. 17–28.
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  • Tsai W.H. (2018). Green production planning and control for the textile industry by using mathematical programming and industry 4.0 techniques, Energies, vol. 11, no. 8, doi: 10.3390/en11082072.
  • Tseng M.L., Lim M. K., Wong W.P., Chen Y.C., and Zhan, Y. (2018). A framework for evaluating the performance of sustainable service supply chain management under uncertainty, Int. J. Prod. Econ., vol. 195, pp. 359–372.
  • Tseng M.L., Wu K.J., Ma L., Kuo T.C., and Sai F. (2019). A hierarchical framework for assessing corporate sustainability performance using a hybrid fuzzy synthetic method-DEMATEL, Technol. Forecast. Soc., vol. 144, pp. 524–533. doi: 10.1016/j.techfore.2017.10.014.
  • Tuyttens D. and Vandaele A. (2010). Using a greedy random adaptative search procedure to solve the cover printing problem, Comput. Oper. Res., vol. 37, no. 4, pp. 640–648.
  • Yiu K.F.C., Mak K.L., and Lau H.Y.K. (2007). A heuristic for the label printing problem, Comput. Oper. Res., vol. 34, no. 9, pp. 2576–2588.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-154fff23-9085-47f3-bf9a-5ca699fd86ae
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