DOI: 10.18178/wcse.2024.06.025
Optimization of Work Shift Scheduling under Multiple Constraints Using Genetic Algorithm
Abstract— Work shift scheduling is an essential task in businesses that incorporate both full-time and parttime workers. Work shifts are managed by the store staff and are influenced by various factors. Therefore, as the number of employees and factors to consider increases, the burden of management also grows. Additionally, the importance of each factor varies depending on the preferences of the individual managing the shifts. Thus, this study aimed to automate the process of work shift scheduling optimally based on the importance of each factor. In this paper, a new optimization method is proposed that considers the priority of each factor, represented by an objective function, using a set of Pareto-optimal solutions. Evaluation experiments were conducted using a simulated store, comparing the results of work shifts obtained by the proposed method to those obtained by the existing weighted sum method. The experimental results showed that the proposed method is particularly effective in optimizing multiple objective functions with strong trade-off relations by considering the priority of each objective function.
Index Terms— Work Shifts Optimization, Genetic Algorithm, Multi-objective Optimization, Pareto-optimal Solutions
Hiroya Akiyama
Shibaura Institute of Technology, JAPAN
Eiji Kamioka
Shibaura Institute of Technology, JAPAN
Chanh Minh Tran
Shibaura Institute of Technology, JAPAN
Phan Xuan Tan
Shibaura Institute of Technology, JAPAN
Cite: Hiroya Akiyama, Eiji Kamioka, Chanh Minh Tran, Phan Xuan Tan, "Optimization of Work Shift Scheduling under Multiple Constraints Using Genetic Algorithm," 2024 The 14th International Workshop on Computer Science and Engineering (WCSE 2024), pp. 162-167, Phuket Island, Thailand, June 19-21, 2024.