As water demand is exponentially increasing and supplies are scarce, integrated urban water management is an important task in all cities and regions. It is a multi-objective problem, because of the many objectives and criteria that should be analyzed. The key goal of this research was to create an integrated decision-making-based multistage scenario-based interval-stochastic programming (IDMSISP) model for urban water management. IDMSISP is a multistage optimization method with two objective functions that can reflect dynamics, uncertainty, and risk analysis in water resource management. This model was developed for the case of study, based on compromise programming. Two prominent objectives involving the value of the benefit of the network and social satisfaction level are examined. The capacity and environment constraints are considered under uncertainty. The uncertainty in these constraints was solved by applying the chance constraint method.

An interval-stochastic compromise programming for urban water resource allocation

Pansera, Bruno Antonio
Methodology
;
Ferrara, Massimiliano
Supervision
2022-01-01

Abstract

As water demand is exponentially increasing and supplies are scarce, integrated urban water management is an important task in all cities and regions. It is a multi-objective problem, because of the many objectives and criteria that should be analyzed. The key goal of this research was to create an integrated decision-making-based multistage scenario-based interval-stochastic programming (IDMSISP) model for urban water management. IDMSISP is a multistage optimization method with two objective functions that can reflect dynamics, uncertainty, and risk analysis in water resource management. This model was developed for the case of study, based on compromise programming. Two prominent objectives involving the value of the benefit of the network and social satisfaction level are examined. The capacity and environment constraints are considered under uncertainty. The uncertainty in these constraints was solved by applying the chance constraint method.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/131434
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