L’objectif de cette étude est de fournir aux investisseurs, aux décideurs politiques et autres des informations sur la manière dont les émissions de gaz à effet de serre (GES) et l’innovation verte affectent les performances financières des entreprises. Bien que les rapports des sociétés de capital-risque sur les émissions de gaz à effet de serre et les innovations vertes aient considérablement augmenté, en particulier au cours des deux dernières décennies, on sait peu de choses sur la manière dont ces deux facteurs affectent les performances financières.
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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
