Skip to main content

Advertisement

SpringerLink
Log in

Multi-objective optimization of lean and resource efficient manufacturing systems

  • Production Management
  • Published:
Production Engineering Aims and scope Submit manuscript

Abstract

In the manufacturing industry, target-oriented and efficient use of resources is gaining importance, alongside economic optimization. The economic and organizational optimization of manufacturing systems according to the lean principles is only partly compatible with the goals of resource-efficient manufacturing. Therefore, an approach is sought to improve individual analyses of manufacturing systems. This paper proposes an approach for the multi-objective optimization of lean and resource-efficient manufacturing systems. To predict the dynamic effects of several configurations of manufacturing systems, material, energy, and information flows of a discrete event simulation are coupled with an assessment model, based on objectives of lean and resource-efficient manufacturing. Using design of experiments, Gaussian process meta-models are computed for the behavior of the simulation model. These meta-models allow the approximation of the system behavior to be computed in a short period of time and enable extensive multi-objective optimization and more adequate decision-making support systems. The proposed approach is tested in the metalworking industry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Abdulmalek FA, Rajgopal J (2007) Analyzing the benefits of lean manufacturing and value stream mapping via simulation: a process sector case study. Int J Prod Econ 107(1):223–236. https://doi.org/10.1016/j.ijpe.2006.09.009

    Article  Google Scholar 

  2. Banks J (2003) Discrete event simulation. In: Encyclopedia of information systems, Elsevier, Amsterdam, pp 663–671. https://doi.org/10.1016/b0-12-227240-4/00045-9

    Chapter  Google Scholar 

  3. Becker T Energie- und Ressourceneffizienz in der Zylinderkopffertigung. Ph.D. thesis, Berlin. https://www.tib.eu/en/search/id/tema%3ATEMA20140704068/Energie-und-Ressourceneffizienz-in-der-Zylinderkopffertigung

  4. van Beers WCM, Kleijnen JPC (2003) Kriging for interpolation in random simulation. J Oper Res Soc 54(3):255–262. https://doi.org/10.1057/palgrave.jors.2601492

    Article  MATH  Google Scholar 

  5. Bey N, Hauschild MZ, McAloone TC (2013) Drivers and barriers for implementation of environmental strategies in manufacturing companies. CIRP Ann 62(1):43–46. https://doi.org/10.1016/j.cirp.2013.03.001

    Article  Google Scholar 

  6. Brauckmann O (2015) Smart production. Springer, Berlin Heidelberg. https://doi.org/10.1007/978-3-662-45302-5

    Book  Google Scholar 

  7. Derringer G, Suich R (1980) Simultaneous optimization of several response variables. J Qual Technol 12(4):214–219. https://doi.org/10.1080/00224065.1980.11980968

    Article  Google Scholar 

  8. Detty RB, Yingling JC (2000) Quantifying benefits of conversion to lean manufacturing with discrete event simulation: a case study. Int J Prod Res 38(2):429–445. https://doi.org/10.1080/002075400189509

    Article  MATH  Google Scholar 

  9. Diaz-Elsayed N, Jondral A, Greinacher S, Dornfeld D, Lanza G (2013) Assessment of lean and green strategies by simulation of manufacturing systems in discrete production environments. CIRP Ann 62(1):475–478. https://doi.org/10.1016/j.cirp.2013.03.066

    Article  Google Scholar 

  10. Dües CM, Tan KH, Lim M (2013) Green as the new lean: how to use lean practices as a catalyst to greening your supply chain. J Clean Prod 40:93–100. https://doi.org/10.1016/j.jclepro.2011.12.023

    Article  Google Scholar 

  11. Erlach K, Sheehan E (2014) Die CO2-Wertstrom-Methode zur Steigerung von Energie- und Materialeffizienz in der Produktion. ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 109(9):655–658. https://doi.org/10.3139/104.111207

    Article  Google Scholar 

  12. European Commission: Liability - Legislation - Environment - European Commission (2019). https://ec.europa.eu/environment/legal/liability/

  13. Florida R (1996) Lean and green: the move to environmentally conscious manufacturing. Calif Manag Rev 39(1):80–105. https://doi.org/10.2307/41165877

    Article  Google Scholar 

  14. Bergmiller GG, McCright PR (2009) Are lean and green programs synergistic? Proceedings of the 2009 Industrial Engineering Research Conference

  15. Garwood TLea (2018) A review of energy simulation tools for the manufacturing sector. Renew Sustain Energy Rev 81:895–911

    Article  Google Scholar 

  16. Greinacher S, Moser E, Freier J, Müller J, Lanza G (2016) Simulation-based methodology for the application of lean and green strategies depending on external change driver influence. Procedia CIRP 48:242–247. https://doi.org/10.1016/j.procir.2016.03.240

    Article  Google Scholar 

  17. Haag H (2013) Eine Methodik zur modellbasierten Planung und Bewertung der Energieeffizienz in der Produktion. Ph.D. thesis, Stuttgart. https://elib.uni-stuttgart.de/handle/11682/4553

  18. Herrmann C, Thiede S, Stehr J, Bergmann L (2008) An environmental perspective on lean production. In: Manufacturing systems and technologies for the new frontier, Springer, London, pp 83–88. https://doi.org/10.1007/978-1-84800-267-8_16

  19. Hesselbach J, Herrmann C, Detzer R, Martin L, Thiede S, Ludemann B et al (2008) Energy efficiency through optimised coordination of production and technical building services. In: LCE 2008: 15th CIRP international conference on life cycle engineering: conference proceedings, CIRP, p 624

  20. Johnson RT, Montgomery DC, Jones B (2011) An empirical study of the prediction performance of space-filling designs. Int J Exp Des Process Optim 2(1):1. https://doi.org/10.1504/ijedpo.2011.038048

    Article  Google Scholar 

  21. Jondral AG (2013) Simulationsgestützte Optimierung und Wirtschaftlichkeitsbewertung des Lean-Methodeneinsatzes. Shaker,

  22. Junge M (2007) Simulationsgestützte Entwicklung und Optimierung einer energieeffizienten Produktionssteuerung. Kassel Univ. Press, Kassel. https://www.upress.uni-kassel.de/katalog/abstract.php?978-3-89958-301-4

  23. King AA, Lenox MJ (2009) Lean and green? An empirical examination of the relationship between lean production and environmental performance. Prod Oper Manag 10(3):244–256. https://doi.org/10.1111/j.1937-5956.2001.tb00373.x

    Article  Google Scholar 

  24. Larson T, Greenwood R (2004) Perfect complements: synergies between lean production and eco-sustainability initiatives. Environ Qual Manag 13(4):27–36. https://doi.org/10.1002/tqem.20013

    Article  Google Scholar 

  25. Law AM, Kelton WD, Kelton WD (2000) Simulation modeling and analysis, vol 3. McGraw-Hill, New York

    MATH  Google Scholar 

  26. Li W, Alvandi S, Kara S, Thiede S, Herrmann C (2016) Sustainability cockpit: an integrated tool for continuous assessment and improvement of sustainability in manufacturing. CIRP Ann 65(1):5–8. https://doi.org/10.1016/j.cirp.2016.04.029

    Article  Google Scholar 

  27. Liu R, Xie X, Yu K, Hu Q (2019) A review of machine learning for the optimization of production processes. Int J Adv Manuf Technol 104:1889–1902. https://doi.org/10.1007/s00170-019-03988-5

    Article  Google Scholar 

  28. Montevechi JAB, de Pinho AF, Leal F, Marins FAS (2007) Application of design of experiments on the simulation of a process in automotive industry. In: 2007 Winter Simulation Conference. IEEE. https://doi.org/10.1109/wsc.2007.4419779

  29. Morse J (1980) Reducing the size of the nondominated set: pruning by clustering. Comput Oper Res 7(1–2):55–66. https://doi.org/10.1016/0305-0548(80)90014-3

    Article  Google Scholar 

  30. Müller E, Engelmann J, Löffler T, Strauch J (2009) Energieeffiziente Fabriken planen und betreiben. Springer, Berlin Heidelberg. https://doi.org/10.1007/978-3-540-89644-9

    Book  Google Scholar 

  31. Neugebauer R, Westkämper E, Klocke F, Spath D, Schenk M, Michaelis A, ten Hompel M, Weidner E (2008) Untersuchung zur Energieeffizienz in der Produktion. FhG, München. https://www.tib.eu/de/suchen/id/TIBKAT%3A576816582/Untersuchung-zur-Energieeffizienz-in-der-Produktion/

  32. O’Hagan A (2006) Bayesian analysis of computer code outputs: a tutorial. Reliab Eng Syst Saf 91(10–11):1290–1300. https://doi.org/10.1016/j.ress.2005.11.025

    Article  Google Scholar 

  33. Peter K (2009) Bewertung und Optimierung der Effektivität von Lean-Methoden in der Kleinserienproduktion. Shaker, Aachen

    Google Scholar 

  34. Rivera L, Chen FF (2007) Measuring the impact of lean tools on the cost–time investment of a product using cost–time profiles. Robot Comput Integr Manuf 23(6):684–689. https://doi.org/10.1016/j.rcim.2007.02.013

    Article  Google Scholar 

  35. Berger Roland (2018) Trend Compendium 2030, Megatrend 4 - Climate change and ecosystem at risk. https://www.rolandberger.com/en/Publications/Trend-Compendium-2030-Megatrend-4.html

  36. Rosenman MA, Gero JS (1985) Reducing the Pareto optimal set in multicriteria optimization (with applications to pareto optimal dynamic programming). Eng Optim 8(3):189–206. https://doi.org/10.1080/03052158508902489

    Article  Google Scholar 

  37. Salonitis K, Ball P (2013) Energy efficient manufacturing from machine tools to manufacturing systems. Procedia CIRP 7:634–639. https://doi.org/10.1016/j.procir.2013.06.045

    Article  Google Scholar 

  38. Santner TJ, Williams BJ, Notz WI (2003) The design and analysis of computer experiments. Springer, New York. https://doi.org/10.1007/978-1-4757-3799-8

    Book  MATH  Google Scholar 

  39. Smew W, Young P, Geraghty J (2013) Supply chain analysis using simulation, gaussian process modelling and optimisation. Int J Simul Model 12(3):178–189. https://doi.org/10.2507/ijsimm12(3)4.239

    Article  Google Scholar 

  40. Sproedt A, Plehn J, Schönsleben P, Herrmann C (2015) A simulation-based decision support for eco-efficiency improvements in production systems. J Clean Prod 105:389–405. https://doi.org/10.1016/j.jclepro.2014.12.082

    Article  Google Scholar 

  41. Thiede S (2012) Energy efficiency in manufacturing systems. Springer, Berlin

    Book  Google Scholar 

  42. Thiede S, Li W, Kara S, Herrmann C (2016) Integrated analysis of energy, material and time flows in manufacturing systems. Procedia CIRP 48:200–205. https://doi.org/10.1016/j.procir.2016.03.248

    Article  Google Scholar 

  43. Thiede S, Seow Y, Andersson J, Johansson B (2013) Environmental aspects in manufacturing system modelling and simulation–state of the art and research perspectives. CIRP J Manuf Sci Technol 6(1):78–87. https://doi.org/10.1016/j.cirpj.2012.10.004

    Article  Google Scholar 

  44. Turek K, Siegel A, Schmidt T (2019) Berücksichtigung elektrischer Lastspitzen in der ereignisdiskreten Simulation eines Materialflusssystems. Simulation in Produktion und Logistik

  45. Weichert D, Link P, Stoll A, Rueping S, Ihlenfeldt S, Wrobel S (2019) A review of machine learning for the optimization of production processes. Int J Adv Manuf Technol 104:1889–1902. https://doi.org/10.1007/s00170-019-03988-5

    Article  Google Scholar 

Download references

Acknowledgements

This research has been supported by the research project LA 2351/40-1 “Methodik zur Mehrzieloptimierung schlanker und ressourceneffizienter Produktionssysteme” of the German Research Foundation. This is gratefully acknowledged.

Author information

Authors and Affiliations

  1. wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131, Karlsruhe, Germany

    Sebastian Greinacher, Leonard Overbeck, Andreas Kuhnle, Carmen Krahe & Gisela Lanza

Authors
  1. Sebastian Greinacher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonard Overbeck
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andreas Kuhnle
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carmen Krahe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gisela Lanza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leonard Overbeck.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Greinacher, S., Overbeck, L., Kuhnle, A. et al. Multi-objective optimization of lean and resource efficient manufacturing systems. Prod. Eng. Res. Devel. 14, 165–176 (2020). https://doi.org/10.1007/s11740-019-00945-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11740-019-00945-9

Keywords

Search

Navigation