Multi-objective Production Systems Optimisation with Investment and Running Cost

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.

[1]  Jan-Eric Ståhl,et al.  A general economic model for manufacturing cost simulation , 2008 .

[2]  Robert S. Kaplan,et al.  Time-Driven Activity-Based Costing: A Simpler and More Powerful Path to Higher Profits , 2007 .

[3]  Minoru Tanaka,et al.  Shifting bottleneck detection , 2002, Proceedings of the Winter Simulation Conference.

[4]  Kalyanmoy Deb,et al.  Simulation-based Innovization for production systems improvement : An industrial case study , 2009 .

[5]  H. Yamashina,et al.  Manufacturing cost deployment , 2002 .

[6]  Aravind Srinivasan,et al.  Innovization: innovating design principles through optimization , 2006, GECCO.

[7]  Fred W. Glover,et al.  Integrating optimization and simulation: research and practice , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[8]  Ulrich von Beck,et al.  The merger of discrete event simulation with activity based costing for cost estimation in manufacturing environments , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[9]  Petter Krus SIMULATION BASED OPTIMISATION FOR SYSTEM DESIGN , 2003 .

[10]  Charles R. Standridge,et al.  Why Lean Needs Simulation , 2006, Proceedings of the 2006 Winter Simulation Conference.

[11]  R. G. Ingalls,et al.  IDENTIFYING COST REDUCTION AND PERFORMANCE IMPROVEMENT OPPORTUNITIES THROUGH SIMULATION , 2010 .

[12]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[13]  Matías Urenda Moris,et al.  Facts Analyser: An Innovative Tool for Factory Conceptual Design Using Simulation , 2007 .