Linkage of Optimization Models

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WP-79-083.pdf

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WP-79-083.pdf

http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/1112 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/1112 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/1112 archive 351 disk0/00/00/11/00 2016-01-15 01:46:14 2021-08-27 17:09:18 2016-01-15 01:46:14 monograph show 2 Kallio M.J. AL0182 Orchard-Hays W. AL0305 Propoi A.I. AL1119 Linkage of Optimization Models pub iis_mod iis_cmp prog_sds The general goal of this article is to investigate the question of how to carry out analysis when a set of mathematical models being used are interdependent. We seek systematic ways of linking such models to each other. The linking approaches should preserve the structure of the original models so that their interpretation during the analysis does not get increasingly complicated. Although the emphasis is on linking two interdependent linear programming models, extensions to multimodel, nonlinear, and stochastic cases can, in principle, be straightforward. The article has been divided into two parts. In the first part we give a precise statement of our interdependent systems. As well, we offer three typical examples of such systems: energy supply--economy, manpower--economy, and forestry--wood processing industry interaction systems. In the second part we consider alternative approaches: classical decomposition principles, approaches derived from nondifferentiable optimization techniques, application of parametric programming techniques as well as the simplex method combined with a partitioning technique. By no means does the paper provide a final solution to our linkage problem. However, our computational experiments indicate that some of the approaches give rise to optimism, while others remain inconclusive. 1979-09 published WP-79-083 WP-79-083 2005 2265 2308 public working_paper IIASA, Laxenburg, Austria 57 FALSE info:eu-repo/semantics/book