@article{128,
  author = {Tad Gonsalves, Kei Yamagishi, Kiyoshi Itoh},
  title = {Multi-Objective Optimization in Service Systems},
  journal = {Journal of Digital Information Management},
  year = {2010},
  volume = {8},
  number = {4},
  doi = {},
  url = {http://www.dline.info/fpaper/jdim/v8i4/5.pdf},
  abstract = {Muti-objective optimization deals with the simultaneous optimization of two or more conflicting 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, conflicting in nature. We use the novel 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. },
}