大规模时变区域覆盖优化建模及其高性能求解

项目名称： 大规模时变区域覆盖优化建模及其高性能求解

项目编号： No.41271400

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 天文学、地球科学

项目作者： 张彤

作者单位： 武汉大学

项目金额： 75万元

中文摘要： 区域覆盖优化模型是应急资源分配、商业选址和公共交通规划等空间决策应用中常见的空间优化模型。动态交通条件下，区域覆盖优化问题建模远较传统静态模型复杂。同时大规模实际应用中，服务设施以及需求数量巨大，时空分布多样，还需考虑时变不确定性情况，造成优化模型形式复杂，求解困难，难以满足快速优化求解的实际需要。本研究针对动态时变覆盖优化建模以及大规模复杂模型求解两个问题，基于项目组在空间优化、高性能地理计算和交通时空数据分析的有关研究基础，建立顾及交通状态的时变区域覆盖优化模型框架，提出大规模复杂模型的化简与高效求解流程方法，研究精确解和启发式近似解的高性能并行求解方法和策略。本研究拟采用应急医疗服务和公共交通优化改善两个应用问题，通过实际数据验证提出的理论与方法。研究成果将为区域覆盖优化的各种应用提供高效可行的方法与技术支持，并有力地推动空间优化决策和高性能地理计算理论方法的发展。

中文关键词： 区域覆盖优化；时变；大规模；可达；

英文摘要： Regional coverage models are widely used in emergency management, business siting, and public transportation planning. Under dynamic conditions,the modeling of regional coverage problems is much more complicated than its static counterparts.Meanwhile in reality, the large numbers of service facilities and demands, plus their dynamic distribution in geographic space and time-varying uncertainty,optimization models are usually complex. Therefore it is difficult to solve these models efficiently and to meet the practical needs. In this proposal, we aim to tackle the modeling of dynamic time-dependent coverage problems and the solving of large-scale complex coverage models. Based on our previous research experience in spatial optimization, high performance gocomputation, and traffic data analysis, we strive to build a comprehensive regional coverage modeling framework that is capable of handle dynamic traffic conditions. The reduction and efficent solving of large scale complex coverage models will also be investigated. Further, we will focus on the development of parallel algorithms for both exact and heursitic solutions. Finally, emergency medical service location and public transit planning improvement are selected as case studies to demonstrate the proposed methodology. Our research would underpin various region

英文关键词： Regional coverage optimization；time-varying；large-scale；accessibility；