<![CDATA[In the competitive service industry, digital transformation of booking management systems has become essential for maintaining customer loyalty. However, many enterprises still rely on manual methods that result in high latency, queue congestion, and imbalanced technician workloads. This study aims to address these inefficiencies by implementing the Particle Swarm Optimization (PSO) algorithm within a web-based service booking system architecture. PSO, a metaheuristic algorithm inspired by the social behavior of animal swarms, is employed to search for globally optimal solutions in a multidimensional search space. The algorithm is configured with 20 particles, a maximum of 100 iterations, and parameters c1 = 2.0, c2 = 2.0, and w = 0.7 to minimize cumulative customer waiting time while balancing technician task allocation based on technician availability, service duration, and operational hour constraints (08:00–16:00). Empirical testing demonstrated significant improvements in operational performance. Prior to optimization, the total customer waiting time over a three-day observation period reached 380 minutes. Following PSO implementation, waiting time was drastically reduced to 150 minutes, representing a 60.53% reduction (230 minutes saved). These findings confirm that the PSO approach not only delivers rapid and adaptive solutions to real-time data fluctuations but also enhances operational system scalability. This research provides a practical contribution for service management system developers seeking to integrate computational intelligence into the optimization of complex business processes.]]>
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