Association Rules Discovery in Workforce Schedule Database

Abstract—Discovering hidden patterns in large sets of workforce schedules to gain insight into the potential knowledge in workforce schedules are crucial to better understanding the workforce dispatching decision making process, thereby improve workforce allocation and optimization. In this paper, a conceptual framework of the scheduling pattern discovery system is proposed. Association rule extraction methodologies are applied to explore the patterns in workforce schedules generated by a genetic algorithm (GA) based method through maximizing system throughput and machine utilization in a parallel production environment. A rule set scheduler is developed which approximates the genetic algorithm’s functionality furthermore yields problem solutions by means of rules of thumb. Numerical examples illustrate that the discovered scheduling patterns can unveil the relationships existing between the characteristics of workers and machine operations, facilitate managers to enhance workforce assignment and predict system production