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Managing water resources system in a mixed inexact environment using superiority and inferiority measures

机译:使用优劣势方法在混合不精确环境中管理水资源系统

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A superiority-inferiority-based fuzzy-stochastic integer programming (SI-FSIP) method is developed for water resources management under uncertainty. In the SI-FSIP method, techniques of fuzzy mathematical programming with the superiority and inferiority measures and joint chance-constrained programming are integrated into an inexact mixed integer linear programming framework. The SI-FSIP improves upon conventional inexact fuzzy programming by directly reflecting the relationships among fuzzy coefficients in both the objective function and constraints with a high computational efficiency, and by comprehensively examining the risk of violating joint probabilistic constraints. The developed method is applied to a case study of water resources planning and flood control within a multi-stream and multi-reservoir context, where several studied cases (including policy scenarios) associated with different joint and individual probabilities are investigated. Reasonable solutions including binary and continuous decision variables are generated for identifying optimal strategies for water allocation, flood diversion and capacity expansion; the tradeoffs between total benefit and system-disruption risk are also analyzed. As the first attempt for planning such a water-resources system through the SI-FSIP method, it has potential to be applied to many other environmental management problems.
机译:针对不确定性条件下的水资源管理,提出了一种基于优劣劣势的模糊随机整数规划(SI-FSIP)方法。在SI-FSIP方法中,将具有优缺点的模糊数学编程技术和联合机会约束编程技术集成到一个不精确的混合整数线性规划框架中。 SI-FSIP通过以高计算效率直接反映目标函数和约束中模糊系数之间的关系,并通过全面检查违反联合概率约束的风险,对传统的不精确模糊编程进行了改进。所开发的方法应用于多流多水库环境下的水资源规划和防洪案例研究,其中研究了与不同联合概率和个体概率相关的几个案例(包括政策情景)。产生了包括二进制和连续决策变量在内的合理解决方案,以确定最佳的水分配,分洪和扩容策略;还分析了总收益与系统破坏风险之间的权衡。作为通过SI-FSIP方法规划这种水资源系统的首次尝试,它有可能被应用于许多其他环境管理问题。

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    MOE Key Laboratory of Regional Energy Systems Optimization, S&C Academy of Energy and Environmental Research, North China Electric Power University, Beijing 102206, China;

    MOE Key Laboratory of Regional Energy Systems Optimization, S&C Academy of Energy and Environmental Research, North China Electric Power University, Beijing 102206, China;

    MOE Key Laboratory of Regional Energy Systems Optimization, S&C Academy of Energy and Environmental Research, North China Electric Power University, Beijing 102206, China;

    Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S0A2, Canada;

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