Multi-Objective Decision Making for the Fire & Rescue Services - A Scoping Study

火灾的多目标决策

• 批准号: EP/G026262/1
• 负责人: John Miles
• 金额: $ 20.24万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FG026262%2F1
• 关键词: Multi; Objective; Decision; Making; Fire

This proposal directly addresses research issues relating to the strategic management of the Fire and Rescue Services (FRS). More specifically it is a scoping bid to determine whether or not Evolutionary Algorithms (EA) can be used to find high performance strategies for a Fire Brigade's deployment of resources to minimise the risk of the loss of life and property. EA are essentially a class of algorithms which are able to cope with challenging, complex problems that cannot be solved by classical optimization and search methods. They have the advantage that they do not need the well defined objective function that is required by other algorithms. Also the cope with conflicting constraints, multi-objective search spaces and other hazards such as discrete variables and non-linearity.The bid arises from the participation by Miles & Rezgui in a joint EPSRC/CLG sandpit to look at research ideas for Integrated Risk Management Planning (IRMP) for the FRS. A scoping bid, with limited research aims, was suggested by the sandpit mentors because they felt that there is a risk that the technology might not be able to cope with the problem domain. The scoping bid will therefore enable the applicants and the sandpit mentors to assess whether or not the proposed strategy can cope with the domain complexity to produce sensible, high performance solutions. This work will be undertaken in collaboration the Building Research Establishment (BRE) and with the Fire and Resilience Directorate of CLG to ensure that the work is relevant to the needs of FRSs. BRE's role is to provide fire engineering expertise.

这项建议直接涉及与消防和救援服务（FRS）的战略管理有关的研究问题。更具体地说，它是一个范围投标，以确定是否可以使用进化算法（EA）来找到高性能的战略，消防队的资源部署，以尽量减少生命和财产损失的风险。EA本质上是一类算法，能够科普经典优化和搜索方法无法解决的具有挑战性的复杂问题。它们的优点是不需要其他算法所需的明确定义的目标函数。此外，科普冲突的约束，多目标搜索空间和其他危险，如离散变量和非线性。该投标来自Miles & Rezgui参与EPSRC/CLG联合沙坑，研究FRS的综合风险管理规划（IRMP）的研究思路。沙坑导师提出了一个研究目标有限的范围投标，因为他们认为存在技术可能无法科普问题领域的风险。因此，范围投标将使申请人和沙坑导师能够评估拟议的策略是否能够科普域的复杂性，以产生合理的，高性能的解决方案。这项工作将与建筑研究机构（BRE）以及CLG的消防和复原力局合作进行，以确保这项工作与FRSs的需求相关。BRE的作用是提供消防工程专业知识。