An evolutionary method with shift pattern learning for real-world multi-skilled personnel scheduling with flexible shifts

Ning Xue, Ruibin Bai, Huan Jin, Tianxiang Cui

Abstract: Personnel scheduling remains a significant organizational challenge with substantial potential for cost and time savings. Despite extensive research in this domain, few studies have been successfully implemented in practice, and even fewer have gained widespread acceptance among end-users. This gap between research and application often arises from oversimplified real-world models, which may result from subjective solution evaluations or a lack of collaboration between modelers and end-users. To bridge this gap, this paper proposes a machine learning-enhanced memetic algorithm (MLMA) that mimics schedules created by experts to solve a highly complex personnel scheduling problem involving multi-skilled workers and flexible shift types (irregular workforce)—a real-world challenge commonly faced in the hospitality sector. By leveraging historical scheduling preferences, the MLMA generates solutions that align with past practices, enhancing their practicality and appeal to end-users. Experiments conducted on real-life instances demonstrate the effectiveness of the proposed approach in addressing real-world problems, where the workforce is predominantly part-time, possesses mixed skills, and requires flexible shifts. Furthermore, the results highlight the MLMA’s ability to identify shift patterns that closely resemble historical schedules, underscoring its potential for practical implementation and its role in bridging the gap between research and real-world application.