Summary
This paper deals with the optimization of the operational costs of service systems. The cost function consists of service costs and waiting costs. Service cost is associated with the employment of service-providing personnel, while the waiting cost is associated with the customers having to wait for the service. The cost function is minimized subject to the server utilization as well as to the customer satisfaction constraints, using the Particle Swarm Optimization (PSO) algorithm. PSO is a fairly recent swarm intelligence meta-heuristic algorithm known for its simplicity in programming and its rapid convergence. The optimization procedure is illustrated with the example of a practical service system. A series of experiments show optimum results for the operation of the service systems.
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Gonsalves, T., Itoh, K. (2008). Cost Minimization in Service Systems Using Particle Swarm Optimization. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70560-4_13
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DOI: https://doi.org/10.1007/978-3-540-70560-4_13
Publisher Name: Springer, Berlin, Heidelberg
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