With improving environmental consciousness and the growing demand for valuable resources, waste recycling has become an important concern. This work studies the profit of recyclers and platforms with a degree of trust-building in the reverse logistics system considering the following scenarios: online recycling platform builds trust or not under centralized and decentralized models. The results show that trust-building can effectively make more revenue for the system of the online recycling platform with enhanced demand if the cost of the trust-building construction 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 find a new decision support 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.
YUAN Zhe - EMLV |
- Research
- Digital Transformation, Logistics and Supply Chain