Utilization of data and information flows for enhancing a chambermaid scheduling decision making in a smart hospitality supply chain.

This study presents a novel approach for chambermaid scheduling that integrates data and information flows through mobile applications and a technique called variable neighborhood strategy adaptive search (VaNSAS). VaNSAS employs two simple heuristics, moving network method (MNM) and simple neighborhood search routine (SNSR), along with a simplified differential evolution (SDE) to create schedules. The study demonstrates the effectiveness of the proposed approach, which outperforms traditional manual methods. The use of VaNSAS results in an average improvement of 184.39% in finding better solutions, a reduction in scheduling time by 175.78%, an enhancement in load balancing among chambermaids by 94.25%, and a productivity increase by 50%. The results contribute to improving overall efficiency in the hotel business by facilitating workforce management and demand uncertainties, reducing workload, enabling control of work duration, and enhancing customer satisfaction by allowing customers to better manage their daily schedules. The integration of data and information flows with VaNSAS for chambermaid scheduling has the potential to revolutionize the hospitality sector. In order to facilitate decision-making for the allocation and scheduling of chambermaids, an Internet of Things integrated chambermaid allocation and scheduling software is adopted, which provides a more efficient approach. [ABSTRACT FROM AUTHOR]