Queuing Models and Subspace Identification in Logistics

  • P. Várlaki
  • T. Vadvári
Keywords: subspace identification, queuing models, supply chains, modeling


In modern logistics it might be helpful to describe the behavior of a complex logistical process as well as to determine the strength of relations between certain parameters of the system. In this paper a subspace identification approach has been applied to estimate the relation between the features of the system based on measured input-output pairs. In order to validate the suitability of the approach for logistical processes a queuing based model has been proposed and used to generate simulation data. Our analysis as well as the obtained results clearly reflect that subspace identification approaches can advantageously be applied to model the relation between certain parameters of the system, nevertheless to characterize the strength of this relation, as well.


Download data is not yet available.


van Overschee P, de Moor BL: Subspace Identification for Linear Systems: Theory - Implementation - Applications. Kluwer Academic Publishers, 2011. DOI: 10.1007/978-1-4613-0465-4

Harmati I, Orbán G, Várlaki P: Takagi-Sugeno Fuzzy Control Models for Large Scale Logistics Systems. International Symposium on Computational Intelligence and Intelligent Informatics, Agadir, pp. 199–203, 2007. DOI: 10.1109/ISCIII.2007.367389

Orbán G, Várlaki P: Fuzzy Modelling for Service Strategy and Operational Control of Loading Systems. Acta Technica Jaurinensis, Series Logistics, Vol. 2, No. 3, pp. 375–391, 2009.

De Lathauwer L, De Moor B, Vandewalle J: A multilinear singular value decomposition. SIAM Journal on Matrix Analysis and Applications, Vol. 21, No. 4, pp. 1253–1278, 2000. DOI: 10.1137/S0895479896305696

Szeidl L, Várlaki P: HOSVD Based Canonical Form for Polytopic Models of Dynamic Systems. Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 13, No. 1, pp. 52–60. 2009.

Nagy S, Petres Z, Baranyi P: TP Tool - a MATLAB Toolbox for TP Model Transformation. Proceedings of 8th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, budapest, pp. 483–495, 2007.

Szeidl L, Baranyi P, Petres Z, Várlaki P: Numerical Reconstruction of the HOSVD Based Canonical Form of Polytopic Dynamic Models. International Symposium on Computational Intelligence and Intelligent Informatics, Agadir, pp. 111–116, 2007. DOI: 10.1109/ISCIII.2007.367372

Gunasekaran A, Patel C, McGaughey RE: A Framework for Supply Chain Performance Measurement. International Journal of Production Economics, Vol. 87, No 3, pp. 333–347, 2004. DOI: 10.1016/j.ijpe.2003.08.003

Yao JS, Lin FT: Constructing a Fuzzy Flow-Shop Sequencing Model Based on Statistical Data. International Journal of Approximate Reasoning, Vol. 29, No 3, pp. 215–234, 2002. DOI: 10.1016/S0888-613X(01)00064-0

Sevastjanov PV, Róg P: Fuzzy Modeling of Manufacturing and Logistic Systems. Mathematics and Computers in Simulation, Vol. 63, No. 6, pp. 569–585, 2003. DOI: 10.1016/S0378-4754(03)00064-8

Baranyi P, Petres Z, Korondi P, Yam Y, Hashimoto H: Complexity Relaxation of the Tensor Product Model Transformation for Higher Dimensional Problems. Asian Journal of Control, Vol. 9, No. 2, pp. 195–200, 2007. DOI: 10.1111/j.1934-6093.2007.tb00323.x

Baranyi P, Tikk D, Yam Y, Patton RJ: From Differential Equations to PDC Controller Design via Numerical Transformation. Computers in Industry, Vol. 51, No. 3, pp. 281–297, 2003. DOI: 10.1016/S0166-3615(03)00058-7

How to Cite
Várlaki, P. and Vadvári, T. (2015) “Queuing Models and Subspace Identification in Logistics”, Acta Technica Jaurinensis, 8(1), pp. pp. 63-76. doi: 10.14513/actatechjaur.v8.n1.352.
Transportation Science, Logistics and Agricultural Engineering