Notre objectif était de comprendre l’efficacité d’une entreprise à attirer ses cibles de clients. Pour ce faire, nous avons développé un modèle qui établit un lien entre les activités de marketing d’une entreprise et la combinaison de clients qui achètent auprès de l’entreprise. La plupart des modèles de marketing demandent simplement comment les activités de marketing d’une entreprise influencent le nombre de clients qui achètent la marque. Nous nous sommes demandé comment les activités de marketing d’une entreprise influencent les types de clients qui achètent la marque.

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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