An analytical framework for optimizing task scheduling and routing in internet installation services

The growing demand for internet installation services in Thailand has led to persistently full schedules, resulting in delays beyond standard working hours or task cancellations. These delays stem from inefficient scheduling, which results in unbalanced task distribution, varying task complexity, and impractical time allocations for cable installation and travel. To address these challenges, this study proposes task allocation optimization techniques to mitigate installation delays. The earliest due date rule is applied to prioritize installation tasks based on customer appointments, and K-medoids clustering is employed to group customer locations and reduce the complexity of a mixed-integer programming model. Unlike prior studies, the capacitated vehicle routing problem of this study is adapted to internet installation services by incorporating real-world operational conditions into both the objective function and the operational constraint set. Numerical experiments demonstrate that the proposed methodology reduces delayed tasks, total time, and total cost by 84.27%, 33.65%, and 37.73%, respectively. Each installation team operates within allotted working hours, with no more than five tasks per day. Overall, the proposed framework effectively eliminates delayed and cancelled installation tasks, thereby improving scheduling efficiency and enhancing operational performance. • Apply clustering and routing methods to reduce service delays and cancellations. • Use a decision model to balance workload across installation teams. • Develop a cost-efficient scheduling system using task optimization. • Integrate spatial data analytics to group customer locations effectively. • Demonstrate results from a real-world case study in service operations.