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.
03:09
Cette étude analyse comment les dirigeants de PME réagissent face à la décarbonation, un enjeu majeur souvent perçu comme contraignant. À partir de 22 entretiens, trois profils apparaissent : opportunistes (pas ou peu d’actions concrètes), analytiques (mesure et optimisation), et systémiques (intégration stratégique et innovation).
Contrairement aux grandes entreprises, les PME ne font pas de “window-dressing” : leurs dirigeants agissent vraiment ou pas du tout.
L’étude montre que le dirigeant est un acteur clé de la transition, et souligne l’importance d’outils simples, d’aides conditionnées, de formations au leadership durable et du rôle des filières métiers pour accompagner la décarbonation.
- Recherche
- Développement Durable et RSE, Gouvernance, Management des PME
