Advertisement

Multi-objective Production Systems Optimisation with Investment and Running Cost

  • Leif Pehrsson
  • Amos H. C. Ng
  • Jacob Bernedixen
Chapter

Abstract

In recent years simulation-based multi-objective optimisation (SMO) of production systems targeting e.g., throughput, buffers and work-in-process (WIP) has been proven to be a very promising concept. In combination with post-optimality analysis, the concept has the potential of creating a foundation for decision support. This chapter will explore the possibility to expand the concept of introducing optimisation of production system cost aspects such as investments and running cost. A method with a procedure for industrial implementation is presented, including functions for running cost estimation and investment combination optimisation. The potential of applying SMO and post-optimality analysis, taking into account both productivity and financial factors for decision-making support, has been explored and proven to be very beneficial for this kind of industrial application. Evaluating several combined minor improvements with the help of SMO has opened the opportunity to identify a set of solutions (designs) with great financial improvement, which are not feasible to be explored by using current industrial procedures.

Keywords

Pareto Front Buffer Capacity Discount Cash Flow Buffer Allocation Production System Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Fu, M. C., Andradóttir, S., Carson, J. S., Glover, F., Harell, C. R., Ho, Y.-C., Kelly, J. P., & Robinson, S. M. (2000). Integrating optimisation and simulation: research and practice. In Proceedings of the 2000 Winter Simulation Conference (pp. 610–616). December 9–12, IEEE, Arlington, VA.Google Scholar
  2. 2.
    Law A. M., & McComas, M. G. (2002). Simulation based optimisation. Proceedings of the 2002 Winter Simulation Conference (pp. 41–44). December 8–11, 2002. WSC2002, San Diego, CA.Google Scholar
  3. 3.
    Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (3rd. ed.). Wiltshire, UK: Wiley.MATHGoogle Scholar
  4. 4.
    Ng, A., Urenda, M., Svensson J., Skoogh, A., & Johansson, B. (2007). FACTS analyser: An innovative tool for factory conceptual design using simulation. In Proceedings of Swedish Production Symposium, August 28–30, 2007, Gothenburg.Google Scholar
  5. 5.
    Pehrsson, L. (2009). Simulation-based optimisation in relation to current production system data and evaluation models, Master Thesis in Manufacturing Engineering, University of Skövde, School of Technology and Society, Skövde.Google Scholar
  6. 6.
    Standridge, C. R., & Marvel, J. H. (2006). Why Lean needs simulation. Proceedings of the 2006 Winter Simulation Conference (pp. 1907–1913). December 3–6, 2006. WSC 2006, Monterey, CA.Google Scholar
  7. 7.
    Deb, K., & Srinivasan, A. (2006). Innovization: Innovating design principles through optimization. Proceedings of the Genetic and evolutionary Computation Conference (GECCO-2006)(pp. 1629–1636). The Association of Computing Machinery (ACM), New York.Google Scholar
  8. 8.
    Ng, A., Deb, K., & Dudas, C. (2009). Simulation-based Innovization for production systems and improvement: An industrial case study. Proceedings of the International 3’rd Swedish Production Symposium (SPS’09),December 2–3, 2009. Göteborg, Sweden .Google Scholar
  9. 9.
    Professional Accountants in Business Committee (2008). International good practice guidance, project appraisal using discounted cash flow. Professional Accountants in Business Committee, International Federation of Accountants, 545 Fifth Avenue, 14th Floor, New York, NY 10017, USA.Google Scholar
  10. 10.
    Professional Accountants in Business Committee (2009). International good practice guidance, evaluating and improving costing in organisations. Professional Accountants in Business Committee, International Federation of Accountants, 545 Fifth Avenue, 14th Floor, New York, NY 10017, USA.Google Scholar
  11. 11.
    Von Beck, U., & Nowak, J W. (2000). The merger of discrete event simulation with activity-based costing for cost estimation in manufacturing environments. Proceedings of the 2000 Winter Simulation Conference, December 10–13, 2000. WSC 2000, Wyndham Palace Resort & Spa, Orlando, FL, USA, ACM.Google Scholar
  12. 12.
    Brown Ethan, J.,& Sturrock, D. (2009). Identifying cost reduction and performance improvement opportunities through simulation. Proceedings of the 2009 Winter Simulation Conference, WSC 2009, December 13–16, 2009. Austin, TX.Google Scholar
  13. 13.
    Yamashina, H., & Kubo, T. (2002). Manufacturing cost deployment. International Journal of Production Research, 40(16), 4077–4091.MATHCrossRefGoogle Scholar
  14. 14.
    Jönsson, M., Andersson, C., & Ståhl, J-E. (2007). A general economic model for manufacturing cost simulation. Presented at the 41st CIRP Conference on Manufacturing Systems, Tokyo 2008.Google Scholar
  15. 15.
    Kaplan, R. S., & Andersson, S. R. (2007). Time-driven activity-based costingA simpler and more powerful path to higher profits, Harvard Business School Publishing Corporation, ISBN-13:978-1-4221-0171-1.Google Scholar
  16. 16.
    CMA Canada (1999). Strategic management series, strategic cost management, measuring the cost of capacity. The Society of Management Accountants of Canada, Mississauga Executive Centre, One Robert Speck Parkway, Suite 1400, Mississauga, ON Canada L4Z 3M3.Google Scholar
  17. 17.
    Nord, C., & Pettersson, B. (1997). Total Productive Maintenance med Erfarenhet från Volvo, IVF Industriforskning och utveckling AB, 1997.Google Scholar
  18. 18.
    Roser, C., Nakano, M., & Tanaka, M. (2002). Shifting bottleneck detection. Proceedings of the 2002 Winter Simulation Conference (pp. 1079–1086). San Diego, CA.Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Leif Pehrsson
    • 1
    • 2
  • Amos H. C. Ng
    • 2
  • Jacob Bernedixen
    • 2
  1. 1.Volvo Car CorporationGöteborgSweden
  2. 2.Virtual System Research CentreUniversity of SkövdeSkövdeSweden

Personalised recommendations