Service cost and waiting time-a multi-objective optimization scenario

The goal of service systems is to provide cost-efficient service to customers, while at the same time, reducing the customer waiting time for service. In general, a low cost in system operation leads to longer waiting times, while a higher cost in system operation leads to shorter waiting times. The two objectives-service cost (operational cost) and waiting time (customer satisfaction) are, therefore, conflicting in nature. In this paper, we cast the problem as a multi-objective optimization problem and use the multi-objective particle swarm optimization (MOPSO) algorithm to optimize the two conflicting objective functions simultaneously. MOPSO is a fairly recent swarm intelligence meta-heuristic algorithm known for its simplicity in programming and its rapid convergence. The multi-objective optimization procedure is illustrated with the example of a practical service system. MOPSO produces a family of well-spread Pareto fronts for the two objective functions in the practical service system.