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25 May 2023 Application of particle swarm optimization algorithm in address, taking food delivery cabinets in communities with high-rise buildings neighbourhoods as an example
Yuxi Li, Ge Zhu, Ji Zheng
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Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127120J (2023) https://doi.org/10.1117/12.2679135
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China
Abstract
With the development of the takeaway industry, the demand for space to deposit takeaway food increases rapidly. Currently, an increasing number of office buildings and communities are equipped with food delivery cabinets. However, the conventionally inconsiderate site selection has incurred problems of poor experience for takeaway consumers. In this case, this paper applied the center-of-gravity method to build models for consumers' complaint rate and distance to the food delivery cabinet. The models are solved using particle swarm optimization (PSO) while they are subject to nonconvex functions due to the large volume of data. Compared with conventional site planning, this approach evades local optimal solution, and PSO starts to converge at a favorable rate from the early phase (the 40th iteration). Therefore, the PSO algorithm is efficient in the site planning practice of food delivery cabinets and generates accurate results, offering a brand-new perspective for it.
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Yuxi Li, Ge Zhu, and Ji Zheng "Application of particle swarm optimization algorithm in address, taking food delivery cabinets in communities with high-rise buildings neighbourhoods as an example", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127120J (25 May 2023); https://doi.org/10.1117/12.2679135
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KEYWORDS
Buildings
Particle swarm optimization
Particles
Data modeling
Mathematical modeling
Matrices
Nonlinear optimization
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