Cette recherche co-écrite avec Davide Nicolini et Mark Thompson vise à réintroduire la critique dans la réflexion sur les systèmes d’information. La hype et les dérives de Cambridge Analytica, des fake news, ou de Chat GPT nous rappellent l’urgence d’avoir un cadre théorique où l’humain est placé au centre sans céder pour autant aux excès anthropocentriques. Nous proposons d’introduire trois logiques (sociales, politiques et fantasmatiques) pour traiter la question suivante : comment la politique affective conditionne-t-elle l’adhésion au design des technologies ?

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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