Workforce scheduling approaches for supporting human-centered algorithmic management in manufacturing: A systematic literature review and a conceptual optimization model

Abstract

Workforce scheduling is becoming increasingly complex and time-consuming as the number of influencing factors increases. Intelligent, algorithmic optimization approaches can support managers in mastering this complex task. Since workforce scheduling influences key elements of work design, it has the potential to promote human-centered work design and thus, e.g., reduce stress and increase job satisfaction. This paper presents a literature review of various factors considered by existing algorithmic scheduling approaches in manufacturing environments. The identified factors are classified based on the dimensions of human-centered work design. The results show that the identified approaches integrate various aspects that can support human-centered workforce scheduling. However, usually only specific aspects are considered (e.g. learning effects) and certain dimensions are not considered at all (e.g. feedback). In order to exploit the potentials of algorithmic management for the design of humane work, an exemplary optimization model is presented and discussed.