01809nas a2200205   4500008004100000245005200041210005100093260000900144300001400153490000600167520115100173653004801324653001701372653002001389653002801409100001801437700001801455700001301473856011701486       2010                        eng d00aMulti-objective optimization in service systems0 aMultiobjective optimization in service systems  c2010  a254 - 2590 v83 aMuti-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.10aMulti-Objective Particle Swarm Optimization10aService cost10aService systems10aSimulation optimization1 aGonsalves, T.1 aYamagishi, K.1 aItoh, K.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960666260&partnerID=40&md5=6941ee36882d582a6672bdc8a5f37555