Discovering Patterns in Workforce Schedules Using Data Mining
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 algorithms 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.
Learning/Forgetting Genetic Algorithm Workforce Cross-Training
Yan Jihong Yan Wufu Wang Pengxiang
Dept.of Industrial Engineering, Harbin Institute of Technology, Harbin P.R.China, 150001 MBA, Harbin University of Science and Technology, Harbin P.R.China, 150001 College of Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, USA P.R.China, 53211
国际会议
天津
英文
2007-10-20（万方平台首次上网日期，不代表论文的发表时间）