Human-Computer Optimisation for Water Systems Planning and Management (HOWS)

水系统规划和管理的人机优化 (HOWS)

• 批准号: EP/P009441/1
• 负责人: Dragan Savic
• 金额: $ 90.33万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP009441%2F1
• 关键词: Human; Computer; Optimisation; Water; Systems

It is widely acknowledged that the water and wastewater infrastructure assets, which communities rely upon for health, economy and environmental sustainability, are severely underfunded on a global scale. For example, a funding gap of nearly $55 billion has been identified by the US EPA (ASCE, 2011). In England and Wales, the total estimated capital value of water utility assets is £254.8 billion (Ofwat, 2015), but between 2010 and 2015 only £12.9 billion was allocated for maintaining and replacing assets. Combined with the drive to reduce customers' bills, there will be even more pressure on water companies to find ways to bridge the gap between the available and required finances. As a result of this it is not surprising that optimisation methods have been extensively researched and applied in this area (Maier et al., 2014).The inability of those methods to include into optimisation 'unquantifiable' or difficult to quantify, yet important considerations, such as user subjective domain knowledge, has contributed to the limited adoption of optimisation in the water industry. Many cognitive and computational challenges accompany the design, planning and management involving complex engineered systems. Water industry infrastructure assets (i.e., water distribution and wastewater networks) are examples of systems that pose severe difficulties to completely automated optimisation methods due to their size, conceptual and computational complexity, non-linear behaviour and often discrete/combinatorial nature. These difficulties have first been articulated by Goulter (1992), who primarily attributed the lack of application of optimisation in water distribution network (WDN) design to the absence of suitable professional software. Although such software is now widely available (e.g., InfoWorks, WaterGems, EPANET, etc.), the lack of user under-standing of capabilities, assumptions and limitations still restricts the use of optimisation by practicing engineers (Walski, 2001). Automatic methods that require a purely quantitative mathematical representation do not leverage human expertise and can only find solutions that are optimal with regard to an invariably over-simplified problem formulation. The focus of the past research in this area has almost exclusively been on algorithmic issues. However, this approach neglects many important human-computer interaction issues that must be addressed to provide practitioners with engineering-intuitive, practical solutions to optimisation problems. This project will develop new understanding of how engineering design, planning and management of complex water systems can be improved by creating a visual analytics optimisation approach that will integrate human expertise (through 'human in the loop' interactive optimisation), IT infrastructure (cloud/parallel computing) and state-of-the-art optimisation techniques to develop highly optimal, engineering intuitive solutions for the water industry.The new approach will be extensively tested on problems provided by the UK water industry and will involve practicing engineers and experts in this important problem domain.

人们普遍承认，社区在健康、经济和环境可持续性方面所依赖的水和废水基础设施资产在全球范围内严重供资不足。例如，美国环保署确定了近550亿美元的资金缺口（ASCE，2011年）。在英格兰和威尔士，水务公用事业资产的总估计资本价值为2548亿英镑（Ofwat，2015年），但在2010年至2015年期间，只有129亿英镑用于维护和更换资产。再加上减少客户账单的努力，水务公司将面临更大的压力，要想办法弥合可用资金和所需资金之间的差距。因此，优化方法已被广泛研究并应用于该领域并不令人惊讶（Maier等人，2014).这些方法无法将“不可量化”或难以量化的优化包括在内，但重要的考虑因素（如用户主观领域知识）导致了水行业对优化的有限采用。涉及复杂工程系统的设计、规划和管理伴随着许多认知和计算挑战。水务行业基础设施资产（即，水分配和废水网络）是系统的例子，由于它们的尺寸、概念和计算复杂性、非线性行为以及通常的离散/组合性质，这些系统对完全自动化的优化方法造成了严重的困难。这些困难首先由Goulter（1992）提出，他主要将配水网络（WDN）设计中缺乏优化应用归因于缺乏合适的专业软件。尽管这样的软件现在广泛可用（例如，InfoWorks、WaterGems、EPANET等），缺乏用户对能力、假设和限制的理解仍然限制了实践工程师对优化的使用（Walski，2001）。需要纯定量数学表示的自动方法不利用人类的专业知识，只能找到关于总是过于简化的问题公式的最佳解决方案。过去在这一领域的研究重点几乎完全集中在算法问题上。然而，这种方法忽略了许多重要的人机交互问题，必须解决，为从业者提供工程直观，实用的解决方案，优化问题。该项目将开发新的理解如何工程设计，规划和复杂的水系统的管理可以通过创建一个可视化的分析优化方法，将整合人类的专业知识，（通过“人在回路”互动优化），IT基础设施（云/并行计算）和最先进的优化技术来开发高度优化的，工程直观的解决方案的水行业。新的方法将广泛测试的问题提供的英国水行业，并将涉及执业工程师和专家在这一重要的问题领域。

项目成果

A Diameter Probability Distribution Genetic Algorithm for Least-cost Water Distribution Network Design

最小成本配水管网设计的直径概率分布遗传算法

• 发表时间: 2019
• 作者: Johns M

Adaptive augmented evolutionary intelligence for the design of water distribution networks

用于供水管网设计的自适应增强进化智能

• DOI: 10.1145/3377930.3390204
• 发表时间: 2020
• 作者: Johns M

Generalising human heuristics in augmented evolutionary water distribution network design optimisation

在增强进化配水管网设计优化中推广人类启发法

• 发表时间: 2019
• 作者: Mahmoud HA

Human-evolutionary problem solving through gamification of a bin-packing problem

通过装箱问题的游戏化解决人类进化问题

• DOI: 10.1145/3319619.3326871
• 发表时间: 2019
• 作者: Ross N

Generating Heuristics to Mimic Experts in Water Distribution Network Optimisation

生成启发式方法来模仿配水网络优化中的专家

• 发表时间: 2018
• 作者: Walker DJ