Genetic Optimization Approach to Construct Schedule for Service Staff

Abstract

Rostering is a complex problem widely analyzed in the optimization area in order to create proper solutions in acceptable duration. After examination of the existing solutions, genetic optimization with greedy approach for schedule construction was proposed for the real-life staff timetable-scheduling problem. The algorithm consists of two steps. In the first step, the greedy approach is used to create an initial in polynomial time depending on the numbers of workers and tasks. In the second step, the genetic optimization is performed with respect to the schedules created initially. Using the proposed approach, it is possible to consider hard and soft requirements, such as staff overtime, preferable but optional tasks, free-time periods etc., as a weighted combination of them by defining weights in the evaluation function next to the proper parameter. The cascaded task assignments enable to consider hard constraints such as workers’ holidays or short non-working periods, minimum break requirements, obligatory working periods and other constraints which appear in real life. The dataset of more than 2000 tasks and 50 flight service staff has been used for testing. The analysis showed that the proposed algorithm can be easily parallelized and adopted to big datasets.