With growing environmental awareness and increasing demand for valuable resources, waste recycling has become a major concern. This study examines the profit of recyclers and platforms by taking into account the level of trust in the reverse logistics system, considering the following scenarios: an online recycling platform builds trust or not, in centralized and decentralized models. The results show that building trust can effectively generate more revenue for the online recycling platform system with increased demand if the cost of building trust is relatively low. The revenue-sharing contract is more profitable than the cost-sharing contract, but fails to achieve optimization in the integrated setting. We propose a new decision-making tool for optimal strategies under different decision-making models.
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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
