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A Service Composition Framework for Decision Making under Uncertainty

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Enterprise Information Systems (ICEIS 2009)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 24))

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Abstract

Proposed and developed is a service composition framework for decision-making under uncertainty, which is applicable to stochastic optimization of supply chains. Also developed is a library of modeling components which include Scenario, Random Environment, and Stochastic Service. Service models are classes in the Java programming language extended with decision variables, assertions, and business objective constructs. The constructor of a stochastic service formulates a recourse stochastic program and finds the optimal instantiation of real values into the service initial and corrective decision variables leading to the optimal business objective. The optimization is not done by repeated simulation runs, but rather by automatic compilation of the simulation model in Java into a mathematical programming model in AMPL and solving it using an external solver.

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References

  1. Fourer, R., Gay, D.M., Kernighan, B.W.: AMPL: A Modeling Language For Mathematical Programming. Brooks/Cole-Thomson Learning, Pacific Grove, VA (2003)

    Google Scholar 

  2. Brooke, A., Kendrick, D., Meeraus, A., Raman, R.: GAMS: A User’s Guide. GAMS Development Corporation (1998)

    Google Scholar 

  3. Birge, J.R., Ho, J.K.: Optimal Flows in Stochastic Dynamic Networks with Congestion. Operations Research 41, 203–216 (1992)

    Article  Google Scholar 

  4. Lucas, C., Messina, E., Mitra, G.: Risk and Return Analysis of a Multi-period Strategic Planning Problem. In: Thomas, L., Christers, A. (eds.) Stochastic Modelling in Innovative Manufacturing, pp. 81–96. Springer, Berlin (1997)

    Google Scholar 

  5. Birge, J.R., Louveaux, F.: Introduction to Stochastic Programming. Springer, New York (1997)

    Google Scholar 

  6. Delft, C.v., Vial, J.-P.: A Practical Implementation of Stochastic Programming: An Application to the Evaluation of Option Contracts in Supply Chains. Automatica 40, 743–756 (2004)

    Article  Google Scholar 

  7. Brodsky, A., Nash, H.: CoJava: Optimization Modeling by Nondeterministic Simulation. In: van Beek, P. (ed.) CP 2005. LNCS, vol. 3709, p. 877. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  8. Law, A.M.: Simulation Modeling & Analysis. Suzanne Jeans, New York (2007)

    Google Scholar 

  9. Fu, M.C.: Optimization for Simulation: Theory vs. Practice. Informs J. on Comp. 14, 192–215 (2002)

    Article  Google Scholar 

  10. Brodsky, A., Al-Nory, M., Nash, H.: Service Composition Language to Unify Simulation and Optimization of Supply Chains. In: 41st Hawaii International Conference on System Sciences. IEEE Press, Hawaii (2008)

    Google Scholar 

  11. Al-Nory, M., Brodsky, A.: Unifying Simulation and Optimization of Strategic Sourcing and Transportation. In: Mason, S.J., HIll, R., Moench, L., Rose, O. (eds.) Winter Simulation Conference, Miami, FL (2008)

    Google Scholar 

  12. Domenica, N.D., Birbilis, G., Mitra, G., Valente, P.: Stochastic Programming and Scenario Generation within a Simulation Framework: An Information System Perspective. SPEPS 15 (2004)

    Google Scholar 

  13. Valente, P., Mitra, G., Poojari, C.A., Kyriakis, T.: Software tools for stochastic programming:a stochastic programming integrated environment (SPInE). Department of Mathematical Sciences, Brunel University, West London, UK (2001)

    Google Scholar 

  14. Karabuk, S.: Extending Algebraic Modeling Languages to Support Algorithm Development for Solving Stochastic Programming Models. IMA J. Manag. Math., 1–21 (2007)

    Google Scholar 

  15. Entriken, R.: Language Constructs for Modeling Stochastic Linear Programs. Ann. Oper. Res. 104, 49–66 (2001)

    Article  Google Scholar 

  16. Fourer, R., Gay, D.M., Kernighan, B.W.: Design Principles and New Developments in the AMPL Modeling Language. In: Kallrath, J. (ed.) Modeling Languages in Mathematical Optimization. Kluwer Academic Publishers, Dordrecht (2003)

    Google Scholar 

  17. Dormer, A., Vazacopoulos, A., Verma, N., Tipi, H.: Modeling and Solving Stochastic Problems in Supply Chain Management using Xpress-SP. In: Supply Chain Optimization, pp. 307–354. Springer, US (2005)

    Chapter  Google Scholar 

  18. Dempster, M.A., Scott, J.E., Thompson, G.W.P.: Stochastic Modelling and Optimization using STOCHASTICS. In: Wallace, S.W., Ziemba, W.T. (eds.) Applications of Stochastic Programming, pp. 137–157. SIAM, Philadelphia (2005)

    Google Scholar 

  19. Gassmann, H.I., Gay, D.M.: An Integrated Modeling Environment for Stochastic Programming. In: Wallace, S.W., Ziemba, W.T. (eds.) Applications of Stochastic Programming, pp. 159–175. SIAM, Philadelphia (2005)

    Google Scholar 

  20. Messina, E., Mitra, G.: Modelling and Analysis of Multistage Stochastic Programming Problems: A Software Environment. Eur. J. Oper. Res. 101, 343–359 (1997)

    Article  Google Scholar 

  21. Ateji: http://www.ateji.com

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Author information

Authors and Affiliations

  1. George Mason University, Virginia, U.S.A.

    Malak Al-Nory & Alexander Brodsky

  2. Adaptive Decisions, Inc., Maryland, U.S.A.

    Alexander Brodsky

  3. Google Inc., California, U.S.A.

    Hadon Nash

Authors
  1. Malak Al-Nory
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  2. Alexander Brodsky
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  3. Hadon Nash
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Editor information

Editors and Affiliations

  1. Department of Systems and informatics, Institute for Systems and Technologies of Information, Control and Communication (INSTICC) and Instituto Politécnico de Setúbal (IPS), Rua do Vale de Chaves, Estefanilha, 2910-761, Setúbal, Portugal

    Joaquim Filipe  & José Cordeiro  & 

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Al-Nory, M., Brodsky, A., Nash, H. (2009). A Service Composition Framework for Decision Making under Uncertainty. In: Filipe, J., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2009. Lecture Notes in Business Information Processing, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01347-8_31

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  • DOI: https://doi.org/10.1007/978-3-642-01347-8_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01346-1

  • Online ISBN: 978-3-642-01347-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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