Development of PSPO Simulation Optimization Algorithm

Document Type: Research Paper

Authors

Industrial Engineering Department, Sharif University of Technology, Tehran 14588-89694, Iran

Abstract

In this article a new algorithm is developed for optimizing computationally expensive simulation models. The optimization algorithm is developed for continues unconstrained single output simulation models. The algorithm is developed using two simulation optimization routines. We employed the nested partitioning (NP) routine for concentrating the search efforts in the regions which are most likely contained the global optimum, and we used the experimental design concept for selecting most promising points. Then we integrated the Particle Swarm Optimization (PSO) routine as the searching mechanism of the developed algorithm to single out the best point (optimal or a near optimal solution). Through these integrations, an algorithm was developed which is capable of optimizing digital simulation models. The efficiency of the developed algorithm was then evaluated through a computational experiment. Ten test problems were selected from the literature and the efficiency of the PSPO algorithm was compared by two well-known algorithms. The result of this experiment revealed that the developed algorithm provided a more accurate result comparing to these algorithms.

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[1] Al-shihabi S. (2004), Ants for sampling in nested partition algorithm; Proceeding of 16th European
Conference on Artificial Intelligence, Valencia, Spain; 11-18.
[2] Al-shihabi S., Ólafsson S. (2004), A hybrid of Nested Partition, Binary Ant System, and linear
programming for the multidimensional knapsack problem; Computer & Operation Research 37(2);
247-255.
[3] Barton R.R., Meckesheimer M. (2006), Metamodel-based simulation optimization; Handbooks in
Operations Research and Management Science13, Elsevier/North Holland.
[4] Chen W., Pi L., Shi L. (2009), Optimization and logistics challenges in the enterprise; Springer
Optimization and its Application 30(2); 229-251.
[5] Eberhart R.C., Kennedy J. (1995), A new optimizer using particle swarm theory; Proceeding of 6th
International Symposium on Micro Machine and Human Science, Nagoya, Japan; 39-43.
[6] Fu M.C., Glover F.W., April J. (2005), Simulation optimization: a review, new developments, and
applications; Proceedings of the 2005 Winter Simulation Conference, New Jersey, USA; 83-95.
[7] Henderson S.G., Nelson B.L. (2006), Handbooks in Operations Research and Management Science13,
Elsevier/North Holland.
[8] Hurrion RD. (1997), An example of simulation optimization using a neural network metamodel:
finding the optimum number of kanbans in a manufacturing system; Journal of the Operational
Research Society 48(11); 1105-1112.
[10] Kabirian A. (2006), A review of current methodologies and developing a new heuristic method of
simulation optimization; M.Sc. Dissertation, Sharif University of Technology, Tehran, Iran.
[11] Kelton W.D., Sadowski R.P., Sturrock D.T. (2007), Simulation with Arena, 4th Ed.; McGraw-Hill.
[12] Kennedy J., Eberhart, R.C. (1995), Particle swarm optimization; Proceeding of the IEEE International
Conference on Neural Network4; 1942-1948.
[13] Kleijnen J.P.C., van Beers W., van Nieuwenhuyse I. (2010), Constrained optimization in expensive
simulation: Novel approach; European Journal Of Operation Research 202(1); 164-174.
[14] Kleijnen J.P.C. (2008), Design and Analysis of Simulation Experiments; New York: Springer Science,
Business Media.
[15] Kleijnen J.P.C. (2008), Response surface methodology for constrained simulation optimization: An
overview; Simulation Modeling Practice and Theory 16(1); 50-64.
[16] Liu H., Maghsoodloo S. (2011), Simulation optimization based on Taylor Kriging and evolutionary
algorithm; Applied soft Computing 11(4); 3451-3462.
[17] Liu M., Nelson B.L., Staum J. (2010), Simulation on demand for pricing many securities; Proceedings
of the 2010 Winter Simulation Conference; 2782-2789.
[18] Shi Y., Eberhart RC. (1998), Modified particle swarm optimizer. THE 1998 IEEE International
Conference on Evolutionary Computation, (ICEC’98); 69-73.
[19] Shi L., Ólafsson S. (1998), Nested partitions method for global optimization; Operation Research
48(3); 390-407.
[20] Shi L., Ólafsson S., and Sun N. (1999), New parallel randomized algorithms for the traveling salesman
problem; Computer and Operation Research 26(4); 371-394.
[21] Shi L., Ólafsson S., and Chenn Q. (1999), A new hybrid optimization algorithm; Computer &
Industrial Engineering 36(2); 409-426.
[22] Shi L., Ólafsson S. (2000), Nested partitions method for stochastic optimization; Methodology and
Computing in Applied Probability 2(3); 271-291.

[23] Shi L., Ólafsson S. (2008), Nested partitions method, theory and applications; International Series In
Operations Research & Management Science, Springer.
[24] Tang Z. B.(1994), Adaptive partitioned random search to global optimization; IEEE Transactions on
Automatic Control 39(1); 2235-2244.
[25] Tekin E., Sabuncuoglu I. (2004), Simulation optimization: a comprehensive review on theory and
applications; IIE Transactions 36(11); 1067–1081.