Journal of Engineering, Project, and Production Management, 2026, 16(1), 2025-170

Population Aging Urban Scheduling Management Algorithm Combining Cluster Decomposition and Improved Hybrid Ant Colony Algorithms

Fei Pang¹, Yingxu Li², Guo Miao³, and Yun Shi⁴

¹ Associate Professor, School of Education Science, Hanshan Normal University, Chaozhou, 52041, China
² Associate Professor, College of Economics Management, Hanshan Normal University, Chaozhou 52041, China
³ Associate Professor, Regional Modernization Institute, Jiangsu Academy of Social Sciences, Nanjing 210046, China
⁴ Lecturer, School of Education Science, Hanshan Normal University, Chaozhou 52041, China, E-mail: YunShiys@outlook.com (corresponding author).

Project Management

Received August 20, 2025; revised September 29, 2025; accepted October 6, 2025

Available online December 14, 2025

Abstract: The aging population is intensifying spatiotemporal imbalances in the supply and demand of urban medical and transportation resources. Traditional static planning algorithms need to adapt to the dynamic, clustered nature of demand from elderly residents. To address the limitations of experience-dependent, non-driven traditional scheduling, this paper proposes a novel urban management algorithm that integrates clustering decomposition with an improved hybrid Ant Colony Optimization (ACO) technique. The proposed framework employs a two-layer clustering model. The first layer utilizes an adaptive K-means++ algorithm to optimize the placement of elderly care service base stations by integrating demand intensity and spatial accessibility. The second layer applies a K-medoids algorithm with a walking distance matrix to achieve balanced task allocation. Building on this structure, we designed an enhanced ant colony optimization algorithm, incorporating a genetic algorithm to solve the shortest-path problem with time windows. Key improvements include adaptive initial pheromone generation, a dynamic volatilization mechanism, and a local search strategy. Experimental results in 20×20 and 30×30 grid scenarios demonstrate that our framework shortens path lengths by 18.0% and increases convergence speed by 140% compared with the traditional Ant Colony Optimization. Taking Shanghai's aging community as an example, the model reduced average response time by 25%, increased resource coverage efficiency by 30%, and significantly improved personal load balancing. This research offers a solution that both combines theoretical innovation and practical value for efficient resource allocation in cities with aging populations.

Keywords: Ant colony algorithm, K-means++, cluster decomposition, population aging, scheduling management.

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Citation: Pang, F., Li, Y., Miao, G., and Shi, Y. (2026). Population Aging Urban Scheduling Management Algorithm Combining Cluster Decomposition and Improved Hybrid Ant Colony Algorithms. Journal of Engineering, Project, and Production Management, 16(1), 2025-170.

DOI: 10.32738/JEPPM-2025-170

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