Multi-Water Resources Optimal Allocation Based on Multi-Objective Uncertain Chance-Constrained Programming Model

Owing to serious water shortages and frequent water waste, water crises have swept the world and become progressively severe. One major question is how to rationally allocate limited water resources to guarantee daily water requirements and achieve sustainable and coordinated development simultaneou...

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Veröffentlicht in:	Water resources management Jg. 34; H. 15; S. 4881 - 4899
Hauptverfasser:	Li, Xiaona, Wang, Xiaosheng, Guo, Haiying, Ma, Weimin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.12.2020 Springer Nature B.V
Schlagworte:	administrative management Atmospheric Sciences China Civil Engineering Climate change cost effectiveness Dynamic programming Earth and Environmental Science Earth Sciences Economic models Economics Environment Geotechnical Engineering & Applied Earth Sciences Groundwater Hydrogeology Hydrology/Water Resources issues and policy Linear programming Multiple objective analysis Optimization techniques Parameter uncertainty Rain Random variables Reclaimed water risk Supply & demand supply balance Sustainable development uncertainty wastes wastewater treatment Water consumption Water crises Water requirements Water resources Water reuse Water shortages Water supply Water users China Multi-objective uncertain chance-constrained programming (MUCCP) Uncertain variable Water resources optimal allocation (WROA) Uncertainty theory
ISSN:	0920-4741, 1573-1650
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Abstract	Owing to serious water shortages and frequent water waste, water crises have swept the world and become progressively severe. One major question is how to rationally allocate limited water resources to guarantee daily water requirements and achieve sustainable and coordinated development simultaneously. The combined use of different sources, such as diverted water and local water including reclaimed water, surface and ground water, within a region is an efficacious means to address the imbalance between water supplying and using. Aiming at managing complex uncertainties existing in water resource systems, this paper seeks for a reasonable distribution plan by a multi-objective uncertain chance-constrained programming (MUCCP) approach between multi-water resources and multiple water users. In this model, we adopt an uncertain variable as a new tool to manage the incertitude in parameters. Meanwhile, the likelihood that something will happen is quantified by the uncertain measure. This proposed MUCCP model sets the economic, social and environmental benefits as objectives with capacities of water supply and demand as uncertain chance constraints. Then, the solution to MUCCP model is obtained by solving its crisp equivalent version. Finally, the model is implemented for determination of optimal allocation policy in Handan City, Hebei Province. The results suggest that the MUCCP model could be employed by managers for practical problems to achieve a trade-off between system cost-effectiveness and default risk under uncertainty.
AbstractList	Owing to serious water shortages and frequent water waste, water crises have swept the world and become progressively severe. One major question is how to rationally allocate limited water resources to guarantee daily water requirements and achieve sustainable and coordinated development simultaneously. The combined use of different sources, such as diverted water and local water including reclaimed water, surface and ground water, within a region is an efficacious means to address the imbalance between water supplying and using. Aiming at managing complex uncertainties existing in water resource systems, this paper seeks for a reasonable distribution plan by a multi-objective uncertain chance-constrained programming (MUCCP) approach between multi-water resources and multiple water users. In this model, we adopt an uncertain variable as a new tool to manage the incertitude in parameters. Meanwhile, the likelihood that something will happen is quantified by the uncertain measure. This proposed MUCCP model sets the economic, social and environmental benefits as objectives with capacities of water supply and demand as uncertain chance constraints. Then, the solution to MUCCP model is obtained by solving its crisp equivalent version. Finally, the model is implemented for determination of optimal allocation policy in Handan City, Hebei Province. The results suggest that the MUCCP model could be employed by managers for practical problems to achieve a trade-off between system cost-effectiveness and default risk under uncertainty.
Author	Wang, Xiaosheng Li, Xiaona Ma, Weimin Guo, Haiying
Author_xml	– sequence: 1 givenname: Xiaona surname: Li fullname: Li, Xiaona organization: School of Economics and Management, Tongji University – sequence: 2 givenname: Xiaosheng orcidid: 0000-0001-6034-9843 surname: Wang fullname: Wang, Xiaosheng email: xswang@hebeu.edu.cn organization: School of Mathematics & Physics, Hebei University of Engineering – sequence: 3 givenname: Haiying surname: Guo fullname: Guo, Haiying organization: School of Mathematics & Physics, Hebei University of Engineering – sequence: 4 givenname: Weimin surname: Ma fullname: Ma, Weimin organization: School of Economics and Management, Tongji University
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SubjectTerms	administrative management Atmospheric Sciences China Civil Engineering Climate change cost effectiveness Dynamic programming Earth and Environmental Science Earth Sciences Economic models Economics Environment Geotechnical Engineering & Applied Earth Sciences Groundwater Hydrogeology Hydrology/Water Resources issues and policy Linear programming Multiple objective analysis Optimization techniques Parameter uncertainty Rain Random variables Reclaimed water risk Supply & demand supply balance Sustainable development uncertainty wastes wastewater treatment Water consumption Water crises Water requirements Water resources Water reuse Water shortages Water supply Water users
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Title	Multi-Water Resources Optimal Allocation Based on Multi-Objective Uncertain Chance-Constrained Programming Model
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