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GUSS: Solving Collections of Data Related Models Within GAMS

Part of the Applied Optimization book series (APOP,volume 104)

Abstract

In many applications, optimization of a collection of problems is required where each problem is structurally the same, but in which some or all of the data defining the instance is updated. Such models are easily specified within modern modeling systems, but have often been slow to solve due to the time needed to regenerate the instance, and the inability to use advance solution information (such as basis factorizations) from previous solves as the collection is processed. We describe a new language extension, GUSS, that gathers data from different sources/symbols to define the collection of models (called scenarios), updates a base model instance with this scenario data and solves the updated model instance and scatters the scenario results to symbols in the GAMSdatabase. We demonstrate the utility of this approach in three applications, namely data envelopment analysis, cross validation and stochastic dual dynamic programming. The language extensions are available for general use in all versions of GAMSstarting with release 23.7.

Keywords

  • Data Envelopment Analysis
  • Data Envelopment Analysis Model
  • Model Instance
  • Solve Statement
  • GAMS Model

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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Acknowledgements

This work is supported in part by Air Force Grant FA9550-10-1-0101, DOE grant DE-SC0002319, and National Science Foundation Grant CMMI-0928023.

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Bussieck, M.R., Ferris, M.C., Lohmann, T. (2012). GUSS: Solving Collections of Data Related Models Within GAMS. In: Kallrath, J. (eds) Algebraic Modeling Systems. Applied Optimization, vol 104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23592-4_3

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