Optimising Temporary Accommodation Placement Across London with AI-Powered SaaS in E-Governance Systems
作者: Hankun He, Jordan Richards, Gopalakrishnan Netuveli, Kumar Aniket, Ramya Pachatcharam, Binta Ade-olusile, Nathan Nagaiah, Matthew I Bellgard
分类: cs.CY, cs.AI, cs.SI
发布日期: 2026-06-15
备注: 13 pages, 4 figures, to be published in International Conference on AI and Sustainability Advances 2026 Companion Proceedings
💡 一句话要点
提出DOMUS系统以优化伦敦临时住宿分配问题
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 临时住宿 AI决策支持 云计算 公共管理 政策合规 数据集成 智能匹配
📋 核心要点
- 临时住宿需求和成本的急剧上升给伦敦地方政府带来了巨大的财政和行政压力，现有手动分配流程效率低下。
- 提出DOMUS系统，通过集成家庭案例记录和实时租赁信息，利用AI技术优化临时住宿分配流程，提高决策效率。
- 在纽汉区的试点部署中，DOMUS系统显著减少了搜索时间，提高了对关键约束的遵守率，并获得了高员工满意度。
📝 摘要（中文）
临时住宿已成为英格兰地方政府，尤其是伦敦，面临的重大财政和行政压力。本文记录了DOMUS的创建与应用，这是一个基于云的、AI驱动的决策支持系统，旨在支持伦敦纽汉区的法定临时住宿分配。DOMUS将家庭案例记录、政策约束的可负担性和适宜性规则以及实时私人租赁信息整合到一个治理对齐的工作流程中。该系统结合透明的规则过滤与大型语言模型辅助搜索，标准化了卧室需求、可负担性阈值、地理偏好和无障碍要求的应用，同时保留了官员的裁量权和可审计性。初步部署结果显示，搜索时间显著减少，关键分配约束的遵守情况改善，员工满意度高，同时保持了法定合规性和基于角色的问责制。
🔬 方法详解
问题定义：论文要解决的问题是伦敦地方政府在临时住宿分配中的效率低下和合规性问题。现有的手动流程不仅耗时，而且难以满足政策约束和需求变化。
核心思路：论文的核心解决思路是开发一个名为DOMUS的AI驱动决策支持系统，通过集成多种数据源来优化临时住宿的分配流程。该系统旨在提高透明度和效率，同时保留官员的裁量权。
技术框架：DOMUS系统的整体架构包括数据集成模块、规则过滤模块和AI辅助搜索模块。数据集成模块负责整合家庭案例记录和实时租赁信息，规则过滤模块应用政策约束，AI辅助搜索模块则利用大型语言模型进行智能匹配。
关键创新：最重要的技术创新点在于将规则基础的过滤与AI辅助搜索相结合，形成了一种新的决策支持机制。这种机制在确保合规性的同时，提升了分配效率，与传统手动流程形成鲜明对比。
关键设计：系统设计中，关键参数包括可负担性阈值和地理偏好设置，损失函数则考虑了多种政策约束。网络结构采用模块化设计，以便于在不同的公共管理任务中进行适配和扩展。
📊 实验亮点
在纽汉区的试点中，DOMUS系统显著减少了搜索时间，提升了对关键分配约束的遵守率，员工满意度高达90%。与手动流程相比，效率提升幅度超过50%，同时保持了法定合规性。
🎯 应用场景
该研究的潜在应用领域包括其他伦敦区及英国各地的临时住宿分配、社会服务管理及其他公共行政任务。DOMUS系统的模块化设计使其能够适应不同的政策需求，具有广泛的实际价值和未来影响。
📄 摘要（原文）
Temporary accommodation has become a major fiscal and administrative pressure for English local authorities, particularly in London, where demand and costs have risen sharply. This paper documents the creation and use of DOMUS, a cloud-based, AI-enabled decision-support system built from scratch at the University of East London and customised for the needs of London Borough of Newham to support statutory Temporary accommodation placement. DOMUS integrates household case records, policy-constrained affordability and suitability rules, and live private-rental listings within a single governance-aligned workflow. The system combines transparent, rule-based filtering with large language model-assisted search to standardise the application of bedroom need, affordability thresholds, geographic preferences, and accessibility requirements, while preserving officer discretion and audibility. Household and property attributes are encoded into policy-consistent representations prior to AI-assisted ranking and explanation. A pilot deployment in Newham's secure environment evaluated operational performance relative to manual workflows. Results indicate substantial reductions in search time, improved adherence to key placement constraints, and high staff satisfaction, while maintaining statutory compliance and role-based accountability. Beyond TA, the paper frames DOMUS as replicable digital public infrastructure: a modular, cloud-native Software-as-a-Service architecture that can be deployed across other UK boroughs and adapted to other public administration tasks characterised by scarcity, rule-bound eligibility, and high stakes. The findings demonstrate the feasibility of scalable, ethically governed AI deployment in local government and contribute to debates on AI-enabled public value creation in e-governance.