Towards a flexible work scheduling: a multi-objective job rotation model and real case application

Nowadays, heterogeneity among workers in terms of experience, productivity and anthropometric data represents one of the biggest management challenges for companies, especially those characterized by a high turnover. Companies continuously try to reach more realistic planning and scheduling decisions in the short-term period, aiming to maximize their efficiency. In such context, Industry 4.0 and smart solutions lead to collect useful data about workers’ experience, ergonomic risks and repetitiveness in performing tasks as well as other limitations and physical diseases which could temporarily affect some of them. Thus, on-site collected data can be included in organizational decisioning and scheduling models to obtain higher and more realistic efficiency levels, as well as a balanced workload among workers and higher working safety conditions, with the possibility to manually add further information by practitioners according to their experience. For this reason, this paper adopts a human-centric perspective to define a methodological strategy to evaluate critical human factors to include in operational decisions and proposes a flexible multi-objective job rotation scheduling model. The mathematical model aims to assign jobs considering simultaneously workers’ experience, physical capacity, and physical limitations by minimizing ergonomic risks, boredom, and maximizing throughput. Moreover, more suitable rest-break plans are investigated. Finally, a numerical application is provided and a parametrical analysis is carried out to provide useful managerial insights and recommendations.