OPTIMISE: An Internet-Based Platform for Metamodel-Assisted Simulation Optimization

  • Amos Ng
  • Henrik Grimm
  • Thomas Lezama
  • Anna Persson
  • Marcus Andersson
  • Mats Jägstam
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 4)

Computer simulation has been described as the most effective tool for de-signing and analyzing systems in general and discrete-event systems (e.g., production or logistic systems) in particular (De Vin et al. 2004). Historically, the main disadvantage of simulation is that it was not a real optimization tool. Recently, research efforts have been focused on integrating metaheuristic algorithms, such as genetic algorithms (GA) with simulation software so that “optimal” or close to optimal solutions can be found automatically. An optimal solution here means the setting of a set of controllable design variables (also known as decision variables) that can minimize or maximize an objective function. This approach is called simulation optimization or simulation-based optimization (SBO), which is perhaps the most important new simulation technology in the last few years (Law and McComas 2002). In contrast to other optimization problems, it is assumed that the objective function in an SBO problem cannot be evaluated analytically but have to be estimated through deterministic/ stochastic simulation.


Pareto Front Winter Simulation Slave Processor Metamodeling Method Optimal Computing Budget Allocation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Andersson M, Persson A, Grimm H, Ng A (2007) Simulation-based scheduling using a genetic algorithm with consideration to robustness: A real-world case study. In: The Proceedings of the 17th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM ’07), 18–20, Philadelphia, USA, in press. JuneGoogle Scholar
  2. 2.
    April J, Better M, Glover F, Kelly J (2004) New advances for marrying simulation and optimization. In: Proc. of the 2004 Winter Simulation Conference, Washington, D.C., 5–8, pp 80–86. DecemberGoogle Scholar
  3. 3.
    Biles WE and Casebier JB (2004) Web based evaluation of material handling alternatives for automated manufacturing: A parallel replications approach. In: Ingalls RG, Rossetti MD, Smith JS, Petters BA (eds) Proc. in 2004 Winter Simulation Conference, Orlando, Florida, USAGoogle Scholar
  4. 4.
    Biles WE, Daniels CM, O’ Donnell TJ (1985) Statistical considerations in simulation on a network of microcomputers. In: Blais GC, Soloman SL, Gantz DT (eds) Proc. of the 1985 Winter Simulation Conference, San Francisco, CaliforniaGoogle Scholar
  5. 5.
    Biles WE and Kleijnen JPC (2005) International collaborations in web-based simulation: a focus on experimental design and optimization. In: Kukl ME, Steiger NM, Armstrong FB Joines JA (eds) Proc. in Winter Simulation Conference, Orlando, Florida, USAGoogle Scholar
  6. 6.
    Boesel J, Bowden RO Jr, Kelly JP, Westwig F (2001) Future of simulation optimization. In: Peters A, Smith JS, Medeiros DJ, and Rohrer MW (eds) Proc. of the Winter Simulation Conference, pp 1466–1469Google Scholar
  7. 7.
    Buzacott JA and Shanthikumar JG (1993) Stochastic Models of Manufacturing Systems, Prentice Hall, New JerseyzbMATHGoogle Scholar
  8. 8.
    De Vin LJ, Ng AHC, Oscarsson J (2004) Simulation based decision support for manufacturing system life cycle management. Journal of Advanced Manufacturing Systems 3(2):115–128CrossRefGoogle Scholar
  9. 9.
    Deb K, Agrawal S, Pratap A, Meyarivan T (2001) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Parallel Problem Solving from Nature VI (PPSN-VI)Google Scholar
  10. 10.
    Fu MC, Andradóttir S, Carson JS, Glover F, Harell CR, Ho YC, Kelly JP Robinson SM (2000) Integrating optimization and simulation: research and practice. In: Joines JA, Barton RR, Kang K, Fishwick PA (eds) Proc. of the Winter Simulation Conference, pp 610–616Google Scholar
  11. 11.
    Fujimoto RM (2000) Parallel and Distributed Simulation Systems. New York, Wiley, 2000Google Scholar
  12. 12.
    Gershwin SB (1987) An efficient decomposition method for the approximate evaluation of tandem queues with finite storage space and blocking. Operation Research 35(2):291–305zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Heidelberger P (1988) Discrete-event simulation and parallel replications: statistical properties. Scientific and Statistical Computing 9:1114–1132zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Hurrion RD and Birgil S (1999) A comparison of factorial and random experimental design methods for the development of regression and neural network simulation metamodels. Journal of Operational Research Society 50:1018–1033zbMATHCrossRefGoogle Scholar
  15. 15.
    Kumara SRT, Lee YH, Tang K, Dodd C, Tew J, Yee ST (2002) Simulation anywhere any time: Web-based simulation implementation for evaluating order-to-delivery systems and processes. In: Yücesan E, Chen CH, Snowdon JL, Charnes JM (eds) Proc. in Winter Simulation Conference, Piscataway, New Jersey, pp 1251–1259Google Scholar
  16. 16.
    Law AM, McComas MG (2002) Simulation-based optimization. In: Yücesan E, Chen CH, Snowdon JL, Charnes JM (eds) Proc. of the 2002 Winter Simulation Conference, 8–11, San Diego, California, pp 41–44 DecGoogle Scholar
  17. 17.
    Madan M, Son YJ, Cho H, Kulvatunyou B (2005) Determination of efficient simulation model fidelity for flexible manufacturing systems. International Journal of Computer Integrated Manufacturing 18(2–3):236–250CrossRefGoogle Scholar
  18. 18.
    Marr C, Storey C, Biles WE, Kleijnen JPC (2000) A Java-based simulation manager for web-based simulation. In: Joines JA, Barton RR, Kang K, Fishwick PA (eds) Proc. in Winter Simulation Conference, Orlando, Florida, USAGoogle Scholar
  19. 19.
    Persson A, Grimm H, Ng A (2006) On-line instrumentation for simulation-based optimization. In: Perrone LF, Wieland FP, Liu L, Lawson BG, Nicol DM, Fujimoto RM, (eds) Proc. of the Winter Simulation Conference, Monterey, CA, USA, 3-6 December, pp 304–311Google Scholar
  20. 20.
    Spinellis DD and Papadopoulos CT (2000) Stochastic algorithms for buffer allocation in reliable production lines. Mathematical Problems in Engineering 5:441–458zbMATHCrossRefGoogle Scholar
  21. 21.
    Stainforth D, Kettleborough J, Martin A, Simpson A, Gillis R, Akkas A, Gault AR, Collins M, Gavaghan D, Allen M (2002) Design principles for public-resource modeling research. 14th IASTED International Conference Parallel and Distributed Computing and Systems, Cambridge, USAGoogle Scholar
  22. 22.
    Voß S and Woodruff DL (2000) Optimization Software Class Libraries. Kluwer Academic Publishers, Secaucus, NJ, USAGoogle Scholar
  23. 23.
    Yoo T, Cho H, Yücesan E (2006) Web service based parallel and distributed simulation experience. WSEAS Transactions on Systems 5(5):973–980Google Scholar
  24. 24.
    zu Eissen SM and Stein B (2006) Realization of Web-based simulation services. Computers in Industry 57:261–271CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Amos Ng
    • 1
  • Henrik Grimm
    • 1
  • Thomas Lezama
    • 1
  • Anna Persson
    • 1
  • Marcus Andersson
    • 1
  • Mats Jägstam
    • 1
  1. 1.Centre for Intelligent AutomationUniversity of SkövdeSweden

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