Service coverage optimization for facility location: considering line-of-sight coverage in continuous demand space

Abstract

The reliable service coverage of many facilities or sensors used in smart city infrastructure is highly susceptible to obstructions in urban environments. Optimizing the line-of-sight (LOS) service coverage is essential to locating these facilities for smarter city services. Despite progression in the maximal coverage location problem (MCLP) model for locating facilities, maximizing the LOS service coverage in continuous demand space for facility location problems remains challenging. This study defined the LOS-constrained MCLPs (LOS-MCLPs) and proposed a service coverage optimization model to solve these LOS-MCLPs. We employed a computational geometry algorithm named the visibility polygon (VP) algorithm to simulate the LOS coverage in two-dimensional (2D) continuous demand space. We then coupled this algorithm with a robust heuristic algorithm to search for the optimal solutions to maximize effective LOS service coverage. An experiment applied the developed model to a Wi-Fi hotspot planning problem. The experimental results demonstrated that the proposed model can obtain optimal solutions for LOS-MCLPs according to the distribution of obstacles. Comparative results show that ignoring the LOS effect in the optimization of LOS-MCLPs might lead to large areas of service dead zones.

Keywords:

• Service coverage
• spatial optimization
• facility location
• line of sight

Acknowledgments

The authors express our sincere appreciation to editors Professor May Yuan and Professor Bo Huang for their guidance and assistance throughout the submission process. We are also deeply grateful to the anonymous reviewer for providing constructive comments and valuable suggestions, which significantly improved the quality of our manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available at https://doi.org/10.6084/m9.figshare.19306649.v1

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [No. 41971336, No. 42230107].

Notes on contributors

Xiaoya Ma

Xiaoya Ma is a lecturer of Geographic Information Science at Yangtze University, Wuhan, China. She is interested in spatial optimization, spatial analysis and geovisualization. She contributed to the conceptualization, methodology, formal analysis, review and editing of this paper.

Xiaoyu Zhang

Xiaoyu Zhang is a postgraduate student in Geographic Information Science at Yangtze University and is interested in spatial optimization; Her contributions to this paper include designing the research methods, implementing the model, conducting all the experiments in the study, and drafting the initial version of the paper.

Xiang Zhao

Xiang Zhao is an associate professor of School of Resource and Environmental Science at Wuhan University, Wuhan, China. He supervised the research and contributed to the conceptualization, methodology, and review and editing of the manuscript for this paper.