Integrating spatial autocorrelation into location-allocation problems

该项目旨在提高网络设施定位问题解决方案的速度和质量，例如定位新的消防站以减少火灾损失或关闭人口下降社区的现有学校。这些被称为位置分配（L-A）问题的挑战非常难以最佳解决。本研究调查的趋势相似或不相似的属性值，如住宅集中在地理上靠近设施服务使用集群。这种聚类称为空间自相关（SA）。这些研究人员将通过确定SA信息如何促进L-A分析中更强大的解决方案来填补空间分析文献中的一个重大空白。这种新的方法将使更好，更快的计算解决方案，以大型L-A问题的位置，公共和私人设施，可翻译到其他研究领域，为社会提供经济效益。描述SA和L-A问题的位置解之间关系的文献很少。本研究旨在分析SA在设施需求变量和L-A问题的解决方案之间的强烈怀疑的关系。本文将利用这种关系来重新表述L-A模型及其计算机求解方法。修订后的问题公式将集中在减少可能的位置解决方案的数量检查，使解决大型L-A问题和其他相关的位置问题变得易于处理和有效。空间统计之间的新接口的调查（例如，SA）和空间优化（例如，L-A）将提供新的知识和令人信服的证据，这将刺激各个学科的进一步研究，包括运筹学、区域科学和地理学。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

This project is about improving the speed and quality of solutions for network facility location problems, such as locating new fire stations to reduce fire loss or closing existing schools in population-declining communities. These challenges called location-allocation (L-A) problems are extremely difficult to solve optimally. This research investigates the tendency for similar or dissimilar attribute values, such as residential housing concentrations to cluster geographically near facility service usage. This clustering is called spatial autocorrelation (SA). These researchers will fill a significant gap in the spatial analysis literature by determining how SA information can facilitate more robust solutions in L-A analyses. This new approach will enable better and faster computational solutions to large L-A problems for location both public and private facilities that are translatable to other research domains, providing economical benefits to society. The literature describing relationships between SA and locational solutions to L-A problems is scant. This research seeks to analyze a strongly suspected relationship between SA in facility demand variables and solutions to L-A problems. It will exploit this relationship to reformulate L-A models and their computer solution methods. Revised problem formulations will focus on the reduction of the number of possible locational solutions examined so that solving large-size L-A problems and other related locational problems becomes tractable and efficient. This investigation of the novel interface between spatial statistics (e.g., SA) and spatial optimization (e.g., L-A) will provide new knowledge and convincing evidence that should spur further research in various disciplines, including operations research, regional science, and geography.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.