Multi-objective Production Systems Optimisation with Investment and Running Cost

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


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.


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.


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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

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