With the development of the social economy and the improvement of the consumption concept, a new business model combining offline and online has been promoted. The warehousing system is one of the important links of commodity production and circulation, which involves storage, sorting, and distribution. It has a significant impact on the operation cost and the efficiency of the whole logistics system. The progress of robot technology, the Internet of things, and artificial intelligence technology promotes the automation and intelligence of storage systems. The Robotic Mobile Fulfillment Systems (RMFS), which takes the automatic guided vehicles (AGVs) as the way of handling and picking, greatly improves the space utilization, operation efficiency, and flexibility of the system. This paper studies the RMFS with fixed shelves and establishes the performance evaluation model of the picking system considering the AGVs congestion by establishing the queuing network. The effectiveness of the model is verified by simulation, and the optimization of system parameter configuration is further discussed according to the experimental data.

With the intensification of competition in the logistics industry, customer experience has become a key factor affecting the service quality of third-party logistics (3PL). This paper constructs a comprehensive evaluation system based on customer experience maps, aiming to comprehensively assess the service quality of 3PL. By analyzing the various touchpoints in the logistics service process, identifying key experience factors, and combining quantitative and qualitative methods for evaluation, this study show that the evaluation system can effectively identify the strengths and weaknesses of service quality and provide a scientific basis for the service improvement of 3PL companies. Compared to traditional models like SERVQUAL, this system integrates customer experience maps to reveal previously unidentified service gaps (e.g., real-time tracking issues).

With the intensification of global climate change, carbon neutrality has become a global priority. As a high-energy-consumption and high-carbon-emission sector, the logistics industry’s green and low-carbon transformation is critical to achieving carbon neutrality goals. Third-party distribution centers (3DC), serving as core nodes in logistics systems, exhibit a strong correlation between operational efficiency and carbon emission levels. This study proposes a multi-objective optimization operational strategy model based on Stackelberg game theory, integrating three key objectives: economic costs, carbon emissions, and service quality. Findings demonstrate that optimizing distribution centers’ operational models, route planning, and energy utilization can significantly reduce carbon emissions while maintaining economic efficiency and customer satisfaction. A case study of a third-party cold chain distribution center validated the model’s effectiveness in cutting carbon emissions, optimizing costs, and enhancing service performance. The research provides theoretical foundations and actionable insights to advance green transformation in the logistics industry.