Abstract: Despite the rise of automation, hand-intensive production systems are still in use due to the need for flexibility and accuracy in completing certain tasks. The impact of learning and fatigue on manual task productivity arouses growing interest. The focus of this study is on a flowshop scheduling problem (FSSP) that considers the effects of learning and fatigue. Firstly, a literature review was conducted on the scheduling problem with learning and deterioration effect. After that, a theoretical approach was used to address the problem of FSSP with learning effect. Mathematical models that minimize the makespan were presented. Exact methods and heuristics were proposed for solving the problem in small and large instances. As fatigue is a type of deterioration, a multi-agent model was proposed to integrate the muscular fatigue into the FSSP by minimizing the total fatigue dose. Finally, the framework was validated by a case study application that took place in a manual picking line. The modeling of learning and fatigue effects and the computation of model parameters from real data were discussed. A bi-objective approach was proposed to minimize both makespan and total fatigue dose simultaneously. Break policies are recommended depending on the company's needs and the objective to prioritize. The aim of this work is to inspire future work that is interested in addressing operations research problems with a responsible incorporation of human factors. ; Malgré l’essor de l’automatisation, les systèmes de production à forte main d’œuvre sont toujours utilisés en raison de la nécessité de flexibilité et de précision dans l’accomplissement de certaines tâches. Dans de tels contextes, on s’intéresse à l'impact de l'apprentissage et de la fatigue sur la productivité des tâches manuelles sur un problème d’ordonnancement de flowshop (FSSP). Dans un premier temps, une revue de la littérature a été réalisée sur le problème d’ordonnancement en considérant les effets d’apprentissage et de détérioration. Une approche théorique a ...
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