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Sustainable Water Allocation Under Multi-disciplinary Framework: Dealing with Uncertainties in Decision Making and Optimization

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Proceedings of the Fourteenth International Conference on Management Science and Engineering Management (ICMSEM 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1190))

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Abstract

Evidence shows that studies on sustainable water allocation require multi-disciplinary expertise to achieve a combined perspective of natural and social science. Inspired by sustainable water engineering, this study incorporates a multi-disciplinary decision making framework for sustainable water allocation, and provides an example to demonstrate the proposed paradigm. Initially, experts in Hydrology, Meteorology, Geography, Water Resource Management, Sociology, Economics, and Environmental Sustainability are gathered in different workshops to discuss suitable criteria for consideration in water allocation schemes. The uncertainties in their linguistic expressions are accounted for by using multiple criteria decision making. Subsequently, the indicators and thresholds determined during the decision making process are transformed into parameters for a robust optimization model. Finally, a sample model that deals with the uncertainties in water allocation is shown. Under the proposed framework, multi-disciplinary expertise is critical and we recommend its use in future water allocation projects.

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Acknowledgements

The work is supported by the National Natural Science Foundation of China (Grant No. 71771157), Funding of Sichuan University (Grant No. skqx201726 and 2019hhs-19), and Social Science Funding of Sichuan Province (Grant No. SC19TJ005).

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Correspondence to Liming Yao .

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Yao, L., Su, Z., Chen, X. (2020). Sustainable Water Allocation Under Multi-disciplinary Framework: Dealing with Uncertainties in Decision Making and Optimization. In: Xu, J., Duca, G., Ahmed, S., García Márquez, F., Hajiyev, A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. https://doi.org/10.1007/978-3-030-49829-0_26

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