SciELO - Scientific Electronic Library Online

 
vol.19 número1Sustainable development: A conceptual framework for the technology management field of knowledge and a departure for further researchBuffer sizing for the critical chain project management method índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Articulo

Indicadores

Links relacionados

  • En proceso de indezaciónCitado por Google
  • En proceso de indezaciónSimilares en Google

Compartir


South African Journal of Industrial Engineering

versión On-line ISSN 1012-277X

S. Afr. J. Ind. Eng. vol.19 no.1 Pretoria  2008

 

Using the population-based incremental learning algorithm with computer simulation: Some applications

 

 

J. Bekker; Y. Olivier

Department of Industrial Engineering, University of Stellenbosch, South Africa jb2@sun.ac.za

 

 


ABSTRACT

The integration of the population-based incremental learning (PBIL) algorithm with computer simulation shows how this particular combination can be applied to find good solutions to combinatorial optimisation problems. Two illustrative examples are used: the classical inventory problem of finding a reorder point and reorder quantity that minimises costs while achieving a required service level (a stochastic problem); and the signal timing of a complex traffic intersection. Any traffic control system must be designed to minimise the duration of interruptions at intersections while maximising traffic throughput. The duration of the phases of traffic lights is of primary importance in this regard.


OPSOMMING

Die integrasie van die population-based incremental learning (PBIL) algoritme met rekenaarsimulasie word bespreek, en daar word getoon hoe hierdie spesifieke kombinasie aangewend kan word om goeie oplossings vir kombinatoriese optimeringsprobleme te vind. Twee voorbeelde dien as illustrasie: die klassieke voorraadprobleem waarin 'n herbestelvlak en herbestelhoeveelheid bepaal moet word om koste te minimeer maar nogtans 'n vasgestelde diensvlak te handhaaf ('n stochastiese probleem); en die bepaling van die seintye van 'n komplekse verkeerskruising. Enige verkeerbeheerstelsel moet ontwerp word om die duur van die vloeionderbrekings by verkeerskruisings te minimeer en verkeerdeurset te maksimeer. Die tydsduur van die fases van verkeersligte is dus baie belangrik.


 

 

“Full text available only in PDF format”

 

 

REFERENCES

[1] Al-Sharhan, S., Karray, F. and Gueaieb, W. 2001. Approach of optimizing computer networks using soft computing techniques. Proceedings of the International Conference on Software, Telecommunications and Computer Networks (SOFTCOM'01), 847-854.         [ Links ]

[2] Andradóttir, S. 1998. A review of simulation optimization techniques. Proceedings of the 1998 Winter Simulation Conference, The Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ 08855-1331, USA, 151-158.         [ Links ]

[3] Baluja, S. 1994. Population based incremental learning: A method for integrating genetic search based function optimisation and competitive learning. Technical Report, CMU-CS-94-163. Carnegie Mellon University, Pittsburgh, PA 15213, USA.         [ Links ]

[4] Banks, J. 1998. Handbook of simulation: Principles, methodology, advances, application, and practice, John Wiley & Sons, Inc., New York, NY.         [ Links ]

[5] Baesler, F. and Sepúlveda, J.A. 2000. Multi-response simulation optimization using stochastic genetic search within a goal programming framework. Proceedings of the 2000 Winter Simulation Conference, The Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ 08855-1331, USA, 788-794.         [ Links ]

[6] Chen, L. and Petroianu, A. 1998. Application of PBIL to the optimization of PSS tuning. 1998 International Conference on Power System Technology Proceedings, 2, 834-838.         [ Links ]

[7] Chou, C., Chen, C. and Li, M.C. 2001. Application of computer simulation to the design of a traffic signal timer, Computers & Industrial Engineering, 39(1-2), 81-94.         [ Links ]

[8] Coit, D.W. and Smith, A.E. 2002. Genetic algorithm to maximize a lower-bound for system time-to-failure with uncertain component Weibull parameters, Computers & Industrial Engineering, 41(4), 423-440.         [ Links ]

[9] Dereli, T. and Filiz, I.H. 1999. Optimisation of process planning functions by genetic algorithms, Computers & Industrial Engineering, 36(2), 281-308.         [ Links ]

[10] Fu, M.C., Glover, F.W. and April, J. 2005. Simulation optimization: A review, new developments, and application. Proceedings of the 2005 Winter Simulation Conference, The Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ 08855-1331, USA, 83-95.         [ Links ]

[11] Glover, F., Kelly, J.P. and Laguna, M. 1999. New advances for wedding optimization and simulation. Proceedings of the 1999 Winter Simulation Conference, 255-260.         [ Links ]

[12] Goldberg, D.E. 1989. Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Boston, MA.         [ Links ]

[13] Gosling, T., Jin, N. and Tsang, E. 2004. Population based incremental learning versus genetic algorithms: Iterated prisoners dilemma. Technical Report, CSM-401. University of Essex, Essex, England.         [ Links ]

[14] Hanna, M.D. and Newman, W.R. 2007. Integrated operations management, 2nd edition, Thomson South-Western, Mason, OH.         [ Links ]

[15] Kreng, V.B. and Lee, T. 2004. Modular product design with grouping genetic algorithm - a case study, Computers & Industrial Engineering, 46(3), 443-460.         [ Links ]

[16] Lacksonen, T. 2001. Empirical comparison of search algorithms for discrete event simulation, Computers & Industrial Engineering, 40(1-2), 133-148.         [ Links ]

[17] Law, A.M. and Kelton, W.D. 2000. Simulation modeling and analysis, 3rd edition, McGraw-Hill, Boston, MA.         [ Links ]

[18] Ólafsson, S. and Kim, J. 2002. Simulation optimization. Proceedings of the 2002 Winter Simulation Conference, The Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ 08855-1331, USA, 79-84.         [ Links ]

[19] Sabra, Wang & Associates. 2003. Signal timing process final report. U.S. Department of Transportation http://ops.fhwa.dot.gov/arterial_mgmt/rpt/sig_tim_proc/index.htm, (accessed 13 June 2007)        [ Links ]

[20] Thomas, G.M., Gerth, R., Velasco, T. and Rabelo, L.C. 1995. Using real-coded genetic algorithms for Weibull parameter estimation, Computers & Industrial Engineering, 29(1-4), 377-381.         [ Links ]

[21] Truong, T. and Azadivar, F. 2003. Simulation based optimization for supply chain configuration design. Proceedings of the 2003 Winter Simulation Conference, The Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ 08855-1331, USA, 1268-1275.         [ Links ]

[22] Walpole, R.E. and Myers, R.H. 1993. Probability and statistics for engineers and scientists, 5th edition. Macmillan Publishing Company, New York, NY.         [ Links ]

[23] Winston, W.L. 1994. Operations research applications and algorithms, 3rd edition, Wadsworth, Inc., Belmont, CA.         [ Links ]

[24] Yang, S. and Yao, X. 2005. Experimental study on population-based incremental learning algorithms for dynamic optimization problems, Soft Computing - A Fusion of Foundations, Methodologies and Applications, 9(11), 815-834.         [ Links ]

[25] Zhai, L., Khoo, L. and Fok, S. 2002. Feature extraction using rough set theory and genetic algorithms - an application for the simplification of product quality evaluation, Computers & Industrial Engineering, 43(4), 661-676.         [ Links ]

[26] Zhou, H., Feng, Y. and Han, L. 2001. The hybrid heuristic genetic algorithm for job shop scheduling, Computers & Industrial Engineering, 40(3), 191-200. 72        [ Links ]

 

 

1 The author was enrolled for a B Eng degree in the Department of Industrial Engineering, Stellenbosch University

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License