Multiobjective Optimal Waste Load Allocation Models for Rivers Using Nondominated Sorting Genetic Algorithm-II
Publication: Journal of Water Resources Planning and Management
Volume 132, Issue 3
Abstract
A multiobjective optimization framework for optimal waste load allocation in rivers is proposed, considering (1) the total treatment cost, (2) the equity among the waste dischargers, and (3) a comprehensive performance measure that reflects the dissolved oxygen (DO) violation characteristics. This framework consists of an embedded river water quality simulator that has a gradually varied flow module and a pollutant transport module, which simulates the transport process including reaction kinetics (in terms of biochemical oxygen demand-DO). The outer shell of the framework consists of the two nonseasonal, deterministic, multiobjective waste load allocation planning models, namely, cost-performance model and cost-equity-performance model. These models are solved using a powerful and recently developed multiobjective genetic algorithm technique known as the Nondominated Sorting Genetic Algorithm-II. The practical utility of the multiobjective framework in decision-making is illustrated through a realistic example of the Willamette River in the state of Oregon.
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Acknowledgments
The writers wish to thank the Indian Institute of Technology Madras, India, for providing the necessary facilities to carry out this research work. The discussions the first writer had with Professor Deb and his team from I.I.T. Kanpur, India, and the clarifications provided by them through e-mail, were very useful in understanding the NSGA-II code and applying the same to this study effectively. The comments of the three anonymous reviewers and the Associate Editor were helpful in improving the application value and the quality of presentation of the paper. The discussions, the reports and materials provided by Dr. Bryan A. Tolson were very useful to the writers in constructing and executing the realistic example. The assistance rendered by the three undergraduate students, R. Srinivasan, Varunraj, and Varun Ramanujam of IIT Madras, India, in making the multiobjective genetic algorithm runs, is acknowledged.
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Published In
Journal of Water Resources Planning and Management
Volume 132 • Issue 3 • May 2006
Pages: 133 - 143
Copyright
© 2006 ASCE.
History
Received: Jun 4, 2004
Accepted: Oct 19, 2005
Published online: May 1, 2006
Published in print: May 2006
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