Les chercheurs des écoles de management acceptent généralement une perspective déterministe du comportement, selon laquelle l’obéissance est le produit de forces sociales agissant sur les individus et causant leur comportement. Cette perspective a reçu une validation empirique dans les célèbres études de Solomon Asch et de Staley Milgram. Cependant, les archives de ces expériences permettent d’interpréter leurs résultats surprenant dans un autre sens : si les personnes qui en furent les sujets se comportèrent comme elles le firent, c’est parce qu’elles croyaient bien faire. En d’autres termes, leur obéissance reflétait un choix libre et délibéré.
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Robotic warehouses have transformed logistics, prioritizing speed and efficiency. However, traditional static priority systems often leave low-priority customers facing excessive delays, raising concerns about fairness. This research, based on Invia, a robotic warehouse company, proposes a dynamic priority allocation model to balance efficiency and fairness. By adjusting order priorities over time, this approach ensures that both high-priority and long-waiting low-priority orders receive timely fulfillment. Through stochastic modeling and simulations, we demonstrate that dynamic prioritization reduces delays compared to static and first-come, first-served (FCFS) models. Case studies in e-commerce and healthcare logistics illustrate the broader impact of fairness in automation. As industries increasingly rely on AI-driven decision-making, the balance between efficiency and equity becomes critical. This research challenges the assumption that robotic warehouses should optimize for speed alone and advocates for a future where fairness plays a central role in automated commerce.
- Recherche
- Logistique et Supply Chain, Transformation Digitale
