Article ID: | iaor20021893 |
Country: | United States |
Volume: | 49 |
Issue: | 2 |
Start Page Number: | 235 |
End Page Number: | 245 |
Publication Date: | Mar 2001 |
Journal: | Operations Research |
Authors: | Lasdon Leon S., Watkins David W., McKinney Daene C., Cai Ximing |
Keywords: | geography & environment |
Nonconvex nonlinear programming (NLP) problems arise frequently in water resources management, e.g., reservoir operations, groundwater remediation, and integrated water quantity and quality management. Such problems are usually large and sparse. Existing software for global optimization cannot cope with problems of this size, while current local sparse NLP solvers, e.g., MINOS or CONOPT, cannot guarantee a global solution. In this paper, we apply the Generalized Benders Decomposition (GBD) algorithm to two large nonconvex water resources models involving reservoir operations and water allocation in a river basin, using an approximation to the GBD cuts proposed by Floudas