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Parametric Optimization of Linear and Non-Linear Models via Parallel Computing to Enhance Web-Spatial DSS Interactivity


  • D. Kremmydas

    (Agricultural University of Athens, Greece)

  • A. Petsakos

    (Agricultural University of Athens, Greece)

  • S. Rozakis

    (Agricultural University of Athens, Greece)


A web based Spatial Decision Support System (web SDSS) has been implemented in Thessaly, the most significant arable cropping region in Greece, to evaluate energy crop supply. The web SDSS uses an optimization module to support the decision process launching mathematical programming (MP) profit maximizing farm models. Energy to biomass raw material cost is provided in supply curve form incorporating physical land suitability for crops, farm structure, and Common Agricultural Policy (CAP) scenarios. To generate biomass supply curves, the optimization problem is parametrically solved for a number of steps within a price range determined by the user. The more advanced technique used to solve the MP model, the higher the delay of response to the user. In this paper, the authors examine how effectively the web SDSS response time can be reduced to the user requests using parallel solving of the corresponding optimization problem. The results are encouraging, since the total solution time drops significantly as the problem’s size increases, improving the users’ experience even when the underlying optimization models use advanced, time demanding modeling techniques. These statements are illustrated by comparing linear and non-linear agricultural sector models.

Suggested Citation

  • D. Kremmydas & A. Petsakos & S. Rozakis, 2012. "Parametric Optimization of Linear and Non-Linear Models via Parallel Computing to Enhance Web-Spatial DSS Interactivity," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 4(1), pages 14-29, January.
  • Handle: RePEc:igg:jdsst0:v:4:y:2012:i:1:p:14-29

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    References listed on IDEAS

    1. Michael Creel & William Goffe, 2008. "Multi-core CPUs, Clusters, and Grid Computing: A Tutorial," Computational Economics, Springer;Society for Computational Economics, vol. 32(4), pages 353-382, November.
    2. Rozakis, Stelios, 2011. "Impacts of flatter rates and environmental top-ups in Greece: A novel mathematical modeling approach," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 12(2), June.
    3. Michael R. Bussieck & Michael C. Ferris & Alexander Meeraus, 2009. "Grid-Enabled Optimization with GAMS," INFORMS Journal on Computing, INFORMS, vol. 21(3), pages 349-362, August.
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    Cited by:

    1. Zafeiriou, Eleni & Petridis, Konstantinos & Karelakis, Christos & Arabatzis, Garyfallos, 2016. "Optimal combination of energy crops under different policy scenarios; The case of Northern Greece," Energy Policy, Elsevier, vol. 96(C), pages 607-616.
    2. Dimitris Kremmydas & Stelios Rozakis & Ioannis N. Athanasiadis, 2015. "Dealing with farm heterogeneity on modeling agricultural policy: An Agent Based Modeling Approach," Working Papers 2015-3, Agricultural University of Athens, Department Of Agricultural Economics.

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices


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