Information Operator Scheduling by Genetic Algorithms

Abstract. In this paper, we discuss an approach to an operator scheduling problem in a large organization over time with the aim of maintaining service quality and reducing total labor costs. We propose a genetic algorithm (GA) with a parameterized fitness function inspired by homotopy methods and with null mutation to handle a variable number of operators. The proposed method is applied to the practical problem of scheduling operators in a telephone information center. Experimental results show that the proposed method performs consistently better than a GA method previously developed.

• Type: Conference
• Year: 1998
• Where: SEAL
• Authors: Takeshi Yamada, Kazuyuki Yoshimura, Ryohei Nakano
• Keywords: Machine Learning, Operator Scheduling Problem, Parameterized Fitness Function, SEAL 1998, Total Labor Costs