Linear Progamming Models for Water Pollution Control

Loucks, Daniel P.; ReVelle, Charles; Lynn, Walter R.

doi:10.1287/mnsc.14.4.b166

Cited by 103 publications

(20 citation statements)

References 5 publications

(1 reference statement)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The empirical model structure and parameter value used to address the research objectives for this paper are based on the models and parameters used in Revelle, Loucks, and Lynn (1968) and Loucks, Revelle, and Lynn (1967). The models used in both of these papers are formulated as linear programming problems for managing water quality in a river basin.…”

Section: Multi-objective Programming Applied In Water Quality Managementmentioning

confidence: 99%

The Taxation of Environmental Pollution: A Model for Tax Revenue-Environmental Quality Tradeoffs

Wu¹,

Willett²

2016

IJFR

View full text Add to dashboard Cite

The paper analyzes the Willamette River in Oregon. Here a model (combining the least-cost model and the constraint method of multi-objective programming) is used to determine the appropriate tax rate on environmental externalities, incorporating both revenue and environmental quality objectives. The study finds the following. (1) By using the optimal tax rate, the appropriate tax revenue is determined. (2) The efficient solution set (including tax revenue and water quality considerations) is found by using differing optimal tax rates. (3) The optimal point (solution) in the efficient solution set is chosen by the geometrical argument approach and trade-off analysis approach.

show abstract

Section: Multi-objective Programming Applied In Water Quality Managementmentioning

confidence: 99%

The Taxation of Environmental Pollution: A Model for Tax Revenue-Environmental Quality Tradeoffs

Wu¹,

Willett²

2016

IJFR

View full text Add to dashboard Cite

show abstract

“…[21] and Loucks et al [51] used linear programming; Converse [13] used dynamic programming; Graves et al [35] used nonlinear programming; Deninger and Su (22] used convex programming; Wanielista and Bauer [86], Joeres et al [41] and Brill and Nakamura [10] used linear mixed-integer programming; Smeers and Tyteca [76] used a 'shortest path algorithm and a reduced gradient method; McConagha and Converse [54], Weeter and Belardi (88], Lauria (48], Camara [11], Melo [56] and Voutchkov and Boulos [80] used various heuristic methods. The acceptance of these methods was limited.…”

Section: Wastewater Systems Optimizationmentioning

confidence: 99%

Water Systems Planning: The Optimization Perspective

Cunha¹

2003

Engineering Optimization

View full text Add to dashboard Cite

The progressive technological and economic development of societies has given rise to a global increase in the demand for water. In their efforts to meet the demand for good quality water, in adequate quantities, when and where it is needed, humans have been tampering with the natural hydrological cycle. Decision models play an important role in dealing with water resources planning problems by integrating simultaneously all the relevant aspects (physical, hydrological, technological, financial, etc.) characterizing these problems. This paper presents an overview, pointing out the new trends regarding the decision models commonly used to solve the most important water systems planning problems. The achievements accomplished during these last three decades and the impacts of the improvements attained in software and hardware are highlighted. The gap between theory and practice in this field, and the reasons for such a circumstance are also discussed.

show abstract

“…The alternative management strategies identified with simple models [see, e.g., Liebman and Lynn, 1966;Loucks et al, 1967;ReVelle et al, 1968;Brill et al, 1976] are analyzed further using sophisticated dynamic water quality models to refine the choice of policy decisions. However, even after re.…”

Section: Introductionmentioning

confidence: 99%

Surface water quality management using a multiple‐realization chance constraint method

Takyi¹,

Lence²

1999

Water Resources Research

View full text Add to dashboard Cite

Abstract. Traditional chance-constrained programming (CCP) and simulationoptimization methods of incorporating input information uncertainty in pollution management models are unsuitable for complex river systems with several critical water quality segments. Using the CCP method, characterization of the joint probability distribution of coefficients of the management model is often difficult because stream information is limited and the model formulation is generally difficult to understand and solve. For the simulation-optimization method most of the solutions produced are inferior. The multiple realization model, which includes several scenarios of design conditions simultaneously in an optimization model, overcomes such weaknesses by not requiring the joint probability distribution of the stochastic model coefficients and by producing noninferior solutions. Heuristic and neural network techniques are developed to reduce the computational time required to solve the multiple realization model, through identification and utilization of only potentially important stream and water quality information that influence the optimal solution. These techniques are applied to develop trade-off relationships between waste treatment cost and reliability of achieving dissolved oxygen objectives for an example river basin. Results show that the heuristic technique is computationally efficient when <1000 realizations are included in the model, while the neural network method is suitable when several thousand realizations are needed to adequately represent the stochastic water quality system.

show abstract

Linear Progamming Models for Water Pollution Control

Cited by 103 publications

References 5 publications

The Taxation of Environmental Pollution: A Model for Tax Revenue-Environmental Quality Tradeoffs

The Taxation of Environmental Pollution: A Model for Tax Revenue-Environmental Quality Tradeoffs

Water Systems Planning: The Optimization Perspective

Surface water quality management using a multiple‐realization chance constraint method

Contact Info

Product

Resources

About