Swarm Intelligence in the Optimization of Concurrent Service Systems

Concurrent service systems are modeled using the Generalized Stochastic Petri Nets (GSPN) to account for the multiple asynchronous activities within the system. The simulated operation of the GSPN modeled system is then optimized using the Particle Swarm Optimization (PSO) meta-heuristic algorithm. The objective function consists of the service costs and the waiting costs. Service cost is the cost of hiring service-providing professionals, while waiting cost is the estimate of the loss to business as some customers might not be willing to wait for the service and may decide to go to the competing organizations. The optimization is subject to the management and to the customer satisfaction constraints. The tailor-made PSO is found to converge rapidly yielding optimum results for the operation of a practical concurrent service system.