Parametric Optimization of Linear and Non-Linear Models via Parallel Computing to Enhance Web-Spatial DSS Interactivity
AbstractA 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 problemâ€™s size increases, improving the usersâ€™ 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by IGI Global in its journal International Journal of Decision Support System Technology (IJDSST).
Volume (Year): 4 (2012)
Issue (Month): 1 (January)
Contact details of provider:
Web page: http://www.igi-global.com
Other versions of this item:
- Dimitris Kremmydas & M.I. Haque & Stelios Rozakis, 2011. "Enhancing Web-Spatial DSS interactivity with parallel computing: The case of bio-energy economic assessment in Greece," Working Papers 2011-2, Agricultural University of Athens, Department Of Agricultural Economics.
- 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
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Michael Creel & William Goffe, 2008. "Multi-core CPUs, Clusters, and Grid Computing: A Tutorial," Computational Economics, Society for Computational Economics, vol. 32(4), pages 353-382, November.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Journal Editor).
If references are entirely missing, you can add them using this form.