Abstract
In the last few decades, there is a huge growth in service industries. Therefore, how to utilize human resources efficiently is an important issue for service industries. Call center operation is one of the most important service operations for today’s company. The focus of this study is to solve a call center agent scheduling problem by using search algorithms. Also, shadow price information is used to help evaluating neighborhood solutions. The objective of our problem is to minimize the manpower shortage cost of various skills demands. Given time-varying skill demands and known patterns in call centers, we have to assign a pattern to each day of each agent under the restriction of government policy, union regulations, and company strategies. This study experiments four search methods. Two of them are traditional tabu search and traditional simulated annealing. The other two methods are tabu search and simulated annealing that are improved by using shadow price information to evaluate the neighborhood solution. The experiment results show that tabu search algorithm with neighborhood solutions evaluated by shadow price performs best among the four methods.