Staff Shifting Scheduling System using Linear Programming Language and Machine Learning for Federal Government Girls’ College Yola
Keywords:
Linear Programming Language, Machine Learning, Staff Shifting, Scheduling System, AlgorithmAbstract
The scheduling problem has been studied for a few decades where many researchers have successfully solved scheduling using different approaches. Several institutes are really quite interested about scheduling issues. To achieve quality service, larger firms must maintain effective employee scheduling. At the cause of this research Linear programming (LP) and Machine learning is a good approach for solving this kind of problems because the two algorithms are of difference in approach. Linear programming deals with allocation of scheduling and machine learning for implementation of the paper. In this paper Linear Programming and Machine Learning can be used in dealing with scheduling issues. It generally shows four distinct scheduling issues that are formulated using Linear Programming and Machine Learning. The experimental results show that the proposed approach is effective and efficient in achieving good solutions for large-scale problem instances. In addition, we give some guidance on how to weigh various employees' working preferences and how to balance labor costs and staff satisfaction. Finally, a case study is given to explain how the proposed model and algorithm work for a call center. This study will help call centers make appropriate decisions when planning their employees.