Integrated capacity planning over highly volatile horizons

Abstract Today production planning has to deal with highly dynamic markets and increasing uncertainties. Moreover, it has to take into account possibilities of the surrounding production network. By combining a queueing theory model with a stochastic, dynamic optimization approach, a method to support decision making in production planning was developed. Hereby, a Markovian Decision Process is solved to find cost minimal policies as reactions to volatile market demands for minimizing costs due to capacity adaptations, changes in process steps, and locations. The method was applied at an automotive supplier to find suitable system configurations and investment decisions for an uncertain future.

[1]  Gisela Lanza,et al.  Supply chain design for the global expansion of manufacturing capacity in emerging markets , 2011 .

[2]  Kai Furmans,et al.  Bedientheoretische Methoden als Hilfsmittel der Materialflußplanung , 2000 .

[3]  Peter Nyhuis,et al.  Changeable Manufacturing - Classification, Design and Operation , 2007 .

[4]  D. Kendall.  Stochastic Processes Occurring in the Theory of Queues and their Analysis by the Method of the Imbedded Markov Chain , 1953 .

[5]  D. J. White,et al.  Further Real Applications of Markov Decision Processes , 1988 .

[6]  Stefan Minner,et al.  Manufacturing capacity planning and the value of multi-stage stochastic programming under Markovian demand , 2010 .

[7]  Horst Tempelmeier,et al.  Practical considerations in the optimization of flow production systems , 2003 .

[8]  E. G. Kyriakidis,et al.  Markov decision models for the optimal maintenance of a production unit with an upstream buffer , 2009, Comput. Oper. Res..

[9]  W. Whitt,et al.  The Queueing Network Analyzer , 1983, The Bell System Technical Journal.

[10]  Tamás Kis,et al.  Aggregation - the key to integrating production planning and scheduling , 2004 .

[11]  Marco Cantamessa,et al.  System Life-Cycle Planning , 2009 .

[12]  László Monostori,et al.  Stochastic Dynamic Production Control by Neurodynamic Programming , 2006 .

[13]  R. Bellman.  Dynamic programming. , 1957, Science.

[14]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[15]  Y. Koren,et al.  Manufacturing capacity planning strategies , 2009 .

[16]  Edilson Fernandes de Arruda,et al.  Stability and optimality of a multi-product production and storage system under demand uncertainty , 2008, Eur. J. Oper. Res..