In urban areas where multiple urban educational facilities (UEFs) are located in close proximity and dismiss students simultaneously, uncoordinated vehicle pickups often lead to localized congestion and prolonged idling. This study addresses this problem by proposing a four-phase pickup scheduling algorithm that enables spatio-temporal coordination across neighboring UEFs. The algorithm consists of four phases: (1) identifying related UEFs based on spatial proximity, (2) triggering batch scheduling when incoming requests reach a calculated threshold, (3) prioritizing requests using different strategies, and (4) assigning pickup intervals under travel-time feasibility and street capacity constraints. The approach was evaluated under various spatial and operational settings and compared with a non-scheduling baseline. Experimental results demonstrated that the proposed scheduling method reduces the variability of parent waiting times, the maximum parent waiting time, and overall makespan. These results highlight the effectiveness of coordinated scheduling across neighboring UEFs in improving pickup efficiency and alleviating localized congestion in urban school neighborhoods, thereby contributing to sustainable cities and communities.
Citation: Yuan-Ko Huang. Pickup scheduling algorithms with spatio-temporal coordination for urban educational facilities[J]. Journal of Industrial and Management Optimization, 2026, 22(6): 2726-2754. doi: 10.3934/jimo.2026100
In urban areas where multiple urban educational facilities (UEFs) are located in close proximity and dismiss students simultaneously, uncoordinated vehicle pickups often lead to localized congestion and prolonged idling. This study addresses this problem by proposing a four-phase pickup scheduling algorithm that enables spatio-temporal coordination across neighboring UEFs. The algorithm consists of four phases: (1) identifying related UEFs based on spatial proximity, (2) triggering batch scheduling when incoming requests reach a calculated threshold, (3) prioritizing requests using different strategies, and (4) assigning pickup intervals under travel-time feasibility and street capacity constraints. The approach was evaluated under various spatial and operational settings and compared with a non-scheduling baseline. Experimental results demonstrated that the proposed scheduling method reduces the variability of parent waiting times, the maximum parent waiting time, and overall makespan. These results highlight the effectiveness of coordinated scheduling across neighboring UEFs in improving pickup efficiency and alleviating localized congestion in urban school neighborhoods, thereby contributing to sustainable cities and communities.
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Yuan-Ko Huang. Pickup scheduling algorithms with spatio-temporal coordination for urban educational facilities[J]. Journal of Industrial and Management Optimization, 2026, 22(6): 2726-2754. doi: 10.3934/jimo.2026100
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