Abstract
A customer call center that provides services with high level of customer satisfaction is crucial to a successful modern company. This study solves the agent shift scheduling problem for a 24-hours call center. The objective of our problem is to minimize the shortage of man hour for customer demands of various skills. Considering time-varying demands, different skills, and various regulations, this study uses meta-heuristic algorithms to find near optimal solutions within a reasonable computation time. There are four algorithms (simulated annealing, tabu search, genetic algorithm, and variable neighborhood search) used to solve the combinatorial decision part of the agent scheduling problem. To compute the minimized shortage costs, each of the four algorithms uses a linear programming formulation to allocate man hours to the requirement of various skills. To compare the performance of these four algorithms, computational experiments are conducted. The results show that simulated annealing performs significantly better than the other algorithms in almost all the tested problems.