Mixed-Integer Chance-Constrained Models for Ground-Water Remediation
Publication: Journal of Water Resources Planning and Management
Volume 124, Issue 5
Abstract
Ground-water remediation optimization models were formulated using a statistical optimization methodology, chance-constrained programming (CCP), to account for uncertainty in the coefficients of the models. Several models were formulated that depended on which set of coefficients were considered uncertain. Such models were either mixed-integer linear programming models or mixed-integer nonlinear programming models. The CCP method transformed the probabilistic models to deterministic models. The deterministic models are easier to solve and use less computer memory and less storage space than probabilistic models. Results are presented that demonstrate the models formulated. The results showed that incorporating uncertainty into a ground-water optimization model using CCP could be a practical method for making decisions on well locations and pumping rates in ground-water remediation.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 124 • Issue 5 • September 1998
Pages: 285 - 294
Copyright
Copyright © 1998 American Society of Civil Engineers.
History
Published online: Sep 1, 1998
Published in print: Sep 1998
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