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Titel: Workforce scheduling incorporating worker skills and ergonomic constraints
VerfasserIn: Rinaldi, Marta
Fera, Marcello
Bottani, Eleonora
Grosse, Eric H.
Sprache: Englisch
Titel: Computers & Industrial Engineering
Bandnummer: 168
Verlag/Plattform: Elsevier
Erscheinungsjahr: 2022
Freie Schlagwörter: Ergonomics
Human skill
Human performance
Workforce assignment
Empirical study
DDC-Sachgruppe: 330 Wirtschaft
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: In the last few decades, studies have demonstrated the correlation between worker well-being and the performance of production systems. This paper addresses the problem of assigning workers to tasks in a workshop system. In this context, recent researches have focused on the ergonomics assessment, often neglecting the evaluation of the workers’ performance. This study aims to formulate a mixed integer linear programming model to solve the workforce scheduling problem and improve the performance of the system integrating ergonomics and human skills. To overcome the complexity of the combinatorial problem, a constructive heuristic procedure is developed. Moreover, a novel approach is proposed to determine the workers’ skills. Human performance is modelled in terms of the time required to perform consecutive tasks, considering different sequences of tasks. In addition, the model was applied to a real case study to verify its feasibility. Different scenarios are tested, considering different levels of exposure to different risk factors. The results indicate that a limited increase in the makespan enables decreasing the risk level and the achievement of an excellent workload balance among workers in terms of time spent in performing tasks. Moreover, the heuristic procedure has demonstrated to perform well on instances of realistic size, and it could be adapted to many manufacturing systems to solve the problem in real industrial contexts.
DOI der Erstveröffentlichung: 10.1016/j.cie.2022.108107
URL der Erstveröffentlichung: https://www.sciencedirect.com/science/article/abs/pii/S0360835222001772
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-372586
hdl:20.500.11880/33774
http://dx.doi.org/10.22028/D291-37258
ISSN: 0360-8352
Datum des Eintrags: 16-Sep-2022
Fakultät: HW - Fakultät für Empirische Humanwissenschaften und Wirtschaftswissenschaft
Fachrichtung: HW - Wirtschaftswissenschaft
Professur: HW - Prof. Dr. Eric Grosse
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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