@article {235,
	title = {Multi-objective optimization in service systems},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Export Date: 10 July 2014},
	month = {2010},
	pages = {254 - 259},
	abstract = {Muti-objective optimization deals with the simultaneous optimization of two or more conficting objective functions in real-life systems. This paper deals with the multi-objective optimization in service systems. 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, conficting in nature. We use the novel Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to optimize the two conficting 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.},
	keywords = {Multi-Objective Particle Swarm Optimization, Service cost, Service systems, Simulation optimization},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960666260\&partnerID=40\&md5=6941ee36882d582a6672bdc8a5f37555},
	author = {Gonsalves, T. and Yamagishi, K. and Itoh, K.}
}
