Online 3D Bin Packing (3D-BP) with robotic arms is crucial for reducing transportation and labor costs in modern logistics. While Deep Reinforcement Learning (DRL) has shown strong performance, it often fails to adapt to real-world short-term distribution shifts, which arise as different batches of goods arrive sequentially, causing performance drops. We argue that the short-term lookahead information available in modern logistics systems is key to mitigating this issue, especially during distribution shifts. We formulate online 3D-BP with lookahead parcels as a Model Predictive Control (MPC) problem and adapt the Monte Carlo Tree Search (MCTS) framework to solve it. Our framework employs a dynamic exploration prior that automatically balances a learned RL policy and a robust random policy based on the lookahead characteristics. Additionally, we design an auxiliary reward to penalize long-term spatial waste from individual placements. Extensive experiments on real-world datasets show that our method consistently outperforms state-of-the-art baselines, achieving over 10% gains under distributional shifts, 4% average improvement in online deployment, and up to more than 8% in the best case--demonstrating the effectiveness of our framework.

For a nationwide logistics transportation system, it is critical to make the vehicle loading plans (i.e., given many packages, deciding vehicle types and numbers) at each sorting and distribution center. This task is currently completed by dispatchers at each center in many logistics companies and consumes a lot of workloads for dispatchers. Existing works formulate such an issue as a cargo loading problem and solve it by combinatorial optimization methods. However, it cannot work in some real-world nationwide applications due to the lack of accurate cargo volume information and effective model design under complicated impact factors as well as temporal correlation. In this paper, we explore a new opportunity to utilize large-scale route and human behavior data (i.e., dispatchers' decision process on planning vehicles) to generate vehicle loading plans (i.e., plans). Specifically, we collect a five-month nationwide operational dataset from JD Logistics in China and comprehensively analyze human behaviors. Based on the data-driven analytics insights, we design a Vehicle Loading Plan learning model, named VeLP, which consists of a pattern mining module and a deep temporal cross neural network, to learn the human behaviors on regular and irregular routes, respectively. Extensive experiments demonstrate the superiority of VeLP, which achieves performance improvement by 35.8% and 50% for trunk and branch routes compared with baselines, respectively. Besides, we deployed VeLP in JDL and applied it in about 400 routes, reducing the time by approximately 20% in creating plans. It saves significant human workload and improves operational efficiency for the logistics company.

With the rapid development of sharing economy, we have access to massive sharing systems such as Uber, Airbnb, and bike sharing nowadays. The sharing economy, at its core, is to achieve efficient use of resources. However, the actual usage of shared resources is still unclear to us. Little measurement or analysis, if any, has been conducted to investigate the resource usage patterns with the large-scale data collected from these sharing systems. In this paper, we first analyze the shared bike usage patterns in three typical bike sharing systems based on 140-month multi-event data. From our data-driven analysis, we found that the most used 20% of shared bikes account for 45% of total usage, while the least used 20% of bikes account for less than 1% of usage. To efficiently utilize shared bikes, we propose a usage balancing design called eShare+ to improve the bike sharing systems by considering both the quality of service and bike maintenance, which includes three key components: (i) a statistical model based on archived data to infer historical usage; (ii) an entropy and contextual LSTM-based prediction model with both real-time and archived data to infer future usage; (iii) a model-driven optimal calibration engine for bike selection to dynamically balance usage. We develop an ID swapping-based evaluation methodology to measure the efficiency of eShare+ with data from three large-scale bike sharing systems including 84,000 bikes and 3,300 service stations. Our results show that eShare+ not only fully utilizes shared bikes with efficient maintenance but also improves the quality of service. In addition, eShare+ also has the potential to be applicable to different fleet sizes.

With the rapid growth of the number of private vehicles, searching for accessible parking spaces becomes intractable for drivers, especially during high-demand hours. In recent years, we are witnessing a number of sharing economy services. Contract parking sharing, as an innovative sharing economy mode, has the potential to alleviate the difficult parking issue and make full use of the urban parking resources. However, the uncertainties of both drivers’ parking demand and owners’ sharing supply make it challenging to achieve efficient sharing. Thanks to IoT technology, many current parking lots now record vehicles’ fine-grained parking data for billing purposes. Leveraging these fine-grained parking data, we exploit available contract parking spaces to share them with drivers that have temporary parking demand. Specifically, we propose$\mathrm{W^{2}}$Parking, awin-win contractparkingsharing system, which includes two key components: (i) an idle time prediction model to estimate available periods of parking spaces and (ii) a parking sharing model to schedule temporary users to have access to these available parking spaces under both demand and supply uncertainties using dynamic programming combined with a 2-approximation algorithm with performance-bound guarantees. we evaluate our system on seven-month real-world parking data from 368 parking lots with 14,704 parking spaces. Extensive experimental results show that our$\mathrm{W^{2}}$Parking achieves more than 90% of accuracy in parking time prediction, and the utilization rate of contract parking spaces is improved by 35%.