Journal article
Optimal river basin water resources allocation considering multiple water sources joint scheduling: A bi-level multi-objective programming with copula-based interval-bistochastic information
Computers & industrial engineering, v 194, 110388
Aug 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The uncertainty of hydrological variables and socio-economic parameters presents new challenges for river basin water resources allocation (RBWRA). Motivated by the pressing need for more efficient and eco-friendly RBWRA solutions, a bi-level multi-objective interval-bistochastic programming (BLMOIBSP) model is proposed. The model aims to achieve optimal balance among efficiency, eco-friendliness and equity, collectively called “3E” in this paper. The basin authority, serving as the leader in RBWRA, prioritizes efficiency and eco-friendliness, aiming to maximize economic benefit and minimize total environmental pollutants. Conversely, the sub-areas, as followers, focus more on benefits, striving to maximize total benefits. Taking into account the needs of each stakeholder in the bi-level structure actually facilitates equity goals in RBWRA. Additionally, based on the joint scheduling of multiple water sources, the model considers dual stochastic hydrological variables and multiple uncertain parameters. The copula function captures the dependence between hydrological stochastic variables. Meanwhile, water demand is set as an interval parameter to address uncertainty, handled by an interval two-stage stochastic programming (ITSP) method. Fusing with a chaotic Aquila Optimizer (CAO) algorithm, a bi-level interactive approach based on satisfactory degree (SD) is employed to solve the model and ensure equity in RBWRA. Furthermore, the case of the Dongjiang River Basin in Guangdong Province, China, is presented in conjunction with a multi-scenario analysis to validate the practicality and effectiveness of the proposed model and its associated problem-solving methods. The results indicate that the proposed model effectively ensures the “3E” objectives of RBWRA. Besides that, ITSP has been demonstrated to reduce the risks associated with the uncertainty in actual water allocation in RBWRA. The proposed bi-level model balances the levels better than a single-level model. When solving the model, the improved CAO algorithm demonstrates better convergence and stability. Finally, the strengths of the proposed model and the potential challenges in practical scenarios are remarked and future research directions are indicated.
•ITSP is utilized to describe the uncertain water demand in RWBRA.•A multi-scenario analysis with copula captures the uncertain hydrologic situations.•A bi-level multi-objective interval-bistochastic programming model is established.•An improved chaotic Aquila Optimizer algorithm is proposed to solve the model.•The Dongjiang River of China is taken as a case study to validate the proposal.
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Details
- Title
- Optimal river basin water resources allocation considering multiple water sources joint scheduling: A bi-level multi-objective programming with copula-based interval-bistochastic information
- Creators
- Yan Tu - Wuhan University of TechnologyYongzheng Lu - Wuhan University of TechnologyYutong Xie - Wuhan University of TechnologyBenjamin Lev - Drexel University, Bennett S. LeBow College of Business
- Publication Details
- Computers & industrial engineering, v 194, 110388
- Publisher
- Elsevier
- Number of pages
- 21
- Grant note
- National Natural Science Foun-dation of China: 71801177 Humanities and So-cial Science Fund of Ministry of Education of China: 18YJC630163 General Open Subject for Hubei Innovation and Development Research Institute: CX2023-2-3
This research was supported by the National Natural Science Foun-dation of China (grant number 71801177) , the Humanities and So-cial Science Fund of Ministry of Education of China (grant number 18YJC630163) , and the General Open Subject for Hubei Innovation and Development Research Institute (grant number CX2023-2-3) .
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:001279222200001
- Scopus ID
- 2-s2.0-85199306792
- Other Identifier
- 991021894605904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Industrial