Optimal allocation of regional water resources under water saving management contract

Water Saving Management Contract (WSMC) is a novel market-oriented business model, which can effectually allay the pressure of the regional water crisis. However, the imbalance of water demand and supply affecting the water-saving effect of WSMC will arise with the improvement of relevant water stan...

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Vydáno v:Journal of data, information and management (Online) Ročník 3; číslo 4; s. 281 - 296
Hlavní autoři: An, Xiaojie, Wang, Xiaosheng, Hu, Hao, Pang, Shujiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.12.2021
Springer Nature B.V
Témata:
Artificial Intelligence
Business and Management
Computational Intelligence
Constraint modelling
Consumption
Credibility
Economic development
Efficiency
Engineering
Epistemology
Genetic algorithms
Mathematical programming
Mathematics
Multiple objective analysis
Original Article
Sorting algorithms
Supply & demand
Water conservation
Water consumption
Water demand
Water resources management
Water shortages
Water supply
Armenia
China
Oman
Uncertain multi-objective programming
NSGA-II Algorithm
Water saving management contract
Water resources optimal allocation
Uncertainty theory
ISSN:2524-6356, 2524-6364
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Shrnutí:Water Saving Management Contract (WSMC) is a novel market-oriented business model, which can effectually allay the pressure of the regional water crisis. However, the imbalance of water demand and supply affecting the water-saving effect of WSMC will arise with the improvement of relevant water standards. Consequently, reallocating the regional water resources under WSMC is one of the crucial issues that should be solved imminently. An uncertain multi-objective programming (UMOP) model is formulated to rationally allocate the regional water resources under WSMC, taking into account the trade-offs among bene?ts of water consumption while satisfying the double control actions and water environment capacity constraints. In the model, the bene?ts of water consumption, the cost of water supply, and other parameters are considered as uncertain variables for the case of lacking sufficient data. Then, the model is transformed into the deterministic multi-objective model via inverse uncertainty distribution, which is worked out with the non-dominated sorting genetic algorithm-II (NSGA-II). Finally, the central area of Handan City in China is taken as a case study to con?rm the feasibility of the model. The results showed that the condition of water shortage has improved dramatically and which suggests the potential application in similar regions for water resources management.
Bibliografie:ObjectType-Article-1
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ISSN:2524-6356
2524-6364
DOI:10.1007/s42488-021-00059-x