Urban sprawl, climate change, and resource scarcity will impact how sustainable cities approach future challenges surrounding the management of water resources. A major component of all urban water systems is distribution, which constitutes approximately 80-85% of the total cost of a water-supply system. Traditionally, water distribution systems (WDS) are designed using the ‘worst scenario,’ or ‘robustness’ to improve system reliability. Deterministic assumptions are historically inaccurate and require a new design approach that recognizes uncertainties and offers more adaptability. A Genetic Algorithm based Flexibility Optimization (GAFO) model is developed in Visual C++ and linked with EPANET for the design of WDS that are more adaptable. Unlike traditional GA optimization, GAFO involves a dynamic decision-making process that recognizes a range of possible future conditions and maximizes the flexibility of a WDS at the lowest cost. The outcome is a WDS that can follow different future trajectories (changing conditions) and generate a staged implementation strategy that allows a stepwise evolution of the WDS over time. The GAFO model was tested on several hypothetical cases and was found to perform well in terms of convergence and flexibility. Compared with conventional, non-flexible designs, cost savings in the range of 35% to 72% were realized.

城市扩张，气候变化和资源短缺将影响可持续城市应对水资源管理未来挑战的方式。所有城市供水系统的主要组成部分是分配，约占供水系统总成本的80-85％。传统上，水分配系统（WDS）使用“最坏的情况”或“稳健性”来设计，以提高系统的可靠性。确定性假设在历史上是不准确的，需要一种新的设计方法来识别不确定性并提供更大的适应性。在Visual C ++中开发了一种基于遗传算法的灵活性优化（GAFO）模型，并将其与EPANET链接，以设计更具适应性的WDS。与传统的GA优化不同，GAFO涉及一个动态的决策过程，该过程可以识别一系列可能的未来条件，并以最低的成本最大化WDS的灵活性。结果是WDS可以遵循不同的未来轨迹（条件变化）并生成分阶段的实施策略，该策略允许WDS随时间逐步发展。GAFO模型在几种假设的情况下进行了测试，发现在收敛性和灵活性方面表现良好。与传统的非柔性设计相比，可节省35％至72％的成本。GAFO模型在几种假设的情况下进行了测试，发现在收敛性和灵活性方面表现良好。与传统的非柔性设计相比，可节省35％至72％的成本。GAFO模型在几种假设的情况下进行了测试，发现在收敛性和灵活性方面表现良好。与传统的非柔性设计相比，可节省35％至72％的成本。