Towards an intelligent graphical interface for linear programming modelling
MetadataShow full item record
The increase of computing power of the microcomputers has stimulated the building of direct manipulation interfaces that allow graphical representation of Linear Programming (LP) models. This work discusses the components of such a graphical interface as the basis for a system to assist users in the process of formulating LP problems. In essence, this work proposes a methodology which considers the modelling task as divided into three stages which are specification of the Data Model, the Conceptual Model and the LP Model. The necessity for using Artificial Intelligence techniques in the problem conceptualisation and to help the model formulation task is illustrated.
How to cite this document
Showing items related by title, author, creator and subject.
Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle Pereira, R. J. ; Bignardi, A. B. ; El Faro, L.; Verneque, R. S.; Vercesi Filho, A. E.; Albuquerque, L. G. (Journal of Dairy Science, 2013) [Artigo]Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy ...
Basic ingredients for mathematical modeling of tumor growth in vitro: Cooperative effects and search for space Costa, F. H S; Campos, M. ; Aiéllo, O. E.; da Silva, M. A A (Journal of Theoretical Biology, 2013) [Artigo]Based on the literature data from HT-29 cell monolayers, we develop a model for its growth, analogous to an epidemic model, mixing local and global interactions. First, we propose and solve a deterministic equation for the ...
Ovaskainen, Otso; Ramos, Danielle Leal ; Slade, Eleanor M.; Merckx, Thomas; Tikhonov, Gleb; Pennanen, Juho; Pizo, Marco Aurélio ; Ribeiro, Milton Cezar ; Morales, Juan Manuel (Ecology, 2019) [Artigo]Joint species distribution modeling has enabled researchers to move from species-level to community-level analyses, leading to statistically more efficient and ecologically more informative use of data. Here, we propose ...