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

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  • D. Kremmydas

    (Agricultural University of Athens, Greece)

  • A. Petsakos

    (Agricultural University of Athens, Greece)

  • S. Rozakis

    (Agricultural University of Athens, Greece)

Abstract

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.

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File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdsst.2012010102
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Bibliographic Info

Article provided by IGI Global in its journal International Journal of Decision Support System Technology (IJDSST).

Volume (Year): 4 (2012)
Issue (Month): 1 (January)
Pages: 14-29

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Handle: RePEc:igg:jdsst0:v:4:y:2012:i:1:p:14-29

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Web page: http://www.igi-global.com

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  1. William L. Goffe & Michael Creel, 2005. "Multi-core CPUs, Clusters and Grid Computing: a Tutorial," Computing in Economics and Finance 2005 438, Society for Computational Economics.
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