Two-Phase Optimization for Shift Scheduling in Individualized Teaching Cram School

A shift schedule is indispensable for managing the work of each employee. However, it is difficult to create a shift schedule manually that takes into account employee preferences. Individualized teaching cram schools also requires the construction of a shift schedule for teachers, but at the very least, the subjects that students take and the subjects that teachers are able to teach. It is thus necessary to construct a schedule that satisfies both requests of teachers and students. In this paper, we propose a two-phase optimization method to find such a schedule: shift scheduling using genetic algorithms and student timetabling using simulated annealing. We demonstrate that our proposed method can construct a valid and feasible schedule for each of teachers and students using an actual dataset.