Peu connue en France, la distinction analytique-synthétique est un des piliers de l’empirisme en général et de la méthode scientifique en particulier. En effet, elle traduit, dans la langue de tous les jours, le fait que la connaissance scientifique provient soit de l’expérience (c’est-à-dire de l’observation), soit de la logique appliquée à l’expérience. Les origines de la distinction remontent au quatorzième siècle et à Guillaume d’Ockham, mais c’est David Hume puis Immanuel Kant qui lui donnèrent son expression définitive.

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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.
YUAN Zhe - EMLV |
- Recherche
- Logistique et Supply Chain, Transformation Digitale