Real-time scheduling for multi-agent call center automation

Abstract

In a call center, service agents with difierent capabilities are available for solving incoming customer problems at any time. To supply quick response and better problem solution to customers, it is necessary to schedule customer problems to appropriate service agents eficiently. We developed SANet, a service agent network for call center, which inte-grates multiple service agents including both software agents and human agents, and employs a broker to schedule customer problems to service agents for better solutions according to their changing capabilities and availability. This paper describes the real-time scheduling method in SA-Net as well as its architecture. There are two phases in our scheduling method. One is problem-type learning. The broker is trained to learn the problem types and hence can decide the type of incoming problems auto-matically. The other is the scheduling algorithm based on problem types, capabilities and availability of service agents. We highlight an applica-tion in which we apply SANet to a call center problem for a cable-TV company. Finally, we support our claims via experimental results and discuss related works.