Abstract
Without sufficient data, consulting experts is a good way to quantify unknown parameters in water resources management which will result in human uncertainty. The aim of this paper is to introduce a new tool-uncertainty theory to deal with such uncertainty which is treated as uncertain variable with uncertainty distribution. And a dependent-chance goal programming (DCGP) model is provided for water resources management under such circumstance. In the model uncertain measure is used to measure possibility that an event will occur which is maximized by minimizing the deviation (positive or negative deviation) from target of objective event under a given priority structure. In the end, the developed model is applied to a numerical example to illustrate the effectiveness of the model. The result obtained contributes to the desired water-allocation schemes for decision-markers.
Highlights
The water resources management aims to enhance the effective use and guarantee sustainable development of water resources, when requirements about water quality and quantity of users are satisfied
Revelle et al [2] minimized the cost of the treatment plants in water quality management by using linear programming approach
Based on uncertainty theory, this paper considers the optimal allocation of water supply systems in uncertain environment
Summary
The water resources management aims to enhance the effective use and guarantee sustainable development of water resources, when requirements about water quality and quantity of users are satisfied. Howard and Shamir [3] established a deterministic linear programming model to study interrelated land and water resource management problem. These approaches above did not take indeterminate factors into consideration. Zeng et al [14] developed a two-stage credibilityconstrained programming with Hurwicz criterion approach to tackle uncertainties presented as probability distributions and fuzzy sets. These existing methods are effective to deal with objective uncertainty based on probability theory and fuzzy set theory.
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