The shipping industry has irreplaceable importance in international trade and commerce. How to dynamically price different containers has long been a hot topic due to its direct connection to the final revenue. Two critical observations have been made after a comprehensive survey within a top liner company, China Ocean Shipping Company (COSCO). (1) Each type of container carried on a liner ship has its maximum capacity. (2) The sales volume is occasionally subject to huge fluctuations due to rare uncontrollable factors, such as COVID. Based on the above two points and the liner routine's periodic nature, we model the dynamic pricing problem as an episodic MDP model integrating with both capacity constraints and adversarial corruption, named C3-MDP. To maximize the cumulative revenue in the C3-MDP setting, we propose a programming framework, Bonus-Exploration based Episodic Programming (BEEP). This framework can directly accommodate the linear programming algorithm to form the algorithm BEEP-LP, which provides the episode-wise greedy optimal strategy. Furthermore, a detailed regret analysis is provided, showing that BEEP-LP has a regret that is sublinear in the number of episodes. Combining deep techniques, we also present an approximation algorithm BEEP-DQN in the case of large state-action space to strike a balance between the running time and the performance. Abundant experiments based on real container sales data exhibit the rationality of C3-MDP and the effectiveness of BEEP.

The sweep coverage problem is one of the important issues in spatial crowdsourcing, which requires task participants to monitor a series of Points of Interest (PoIs) periodically. In this paper, we study the Cooperative Sweep Coverage (CSC) problem with the objective of minimizing the maximum sweep period. We propose an iterative clustering algorithm based on spherical projection, called SP-Cycle. The algorithm firstly projects the points in the 2D space to a spherical surface in the 3D space. It then utilizes a new balancing clustering algorithm and uses an iterative coordinate updating method in the 3D spherical space based on the gradient descent, the aim of which is to use the simple minimum spanning tree length computation in the spherical surface to replace the complex Traveling Salesman Problem (TSP) cycle computation in the original 2D space, which improves the performance while keeping a low computational complexity. After we get the clusters, we can compute the lengths of TSP cycles and enter a new iteration. Experimental results based on synthetic and real-world datasets demonstrate the effectiveness of our proposed algorithm. The code is available at https://github.com/GaoYucen/CSC.

With the growing demand for global trade transportation, the shipping container market has gained an increasingly important position. As a key issue of the market, container pricing is regarded as an important indicator to adjust the market supply and demand as well as the revenue of liner enterprises. Although various methods aimed at increasing enterprise revenue, such as expert pricing and dynamic pricing, have been proposed by industry and academia in recent years, these approaches rarely yield worst-case performance guarantee for the double-sided online scenarios of commodities and buyers.To cater to the double-sided online scenario and provide theoretical performance guarantee, we propose an online learning-based pricing framework named Balancing Inventory and Revenue with -chasing Decider (BIRD). BIRD determines container price by combining advantages of given multiple online pricing strategies. We utilize a strategy selector A to select a proper target strategy and use an ϵ-chasing decider ${{{\mathfrak{D}}}^{Cha\operatorname{s} ing}}$ to determine the price. BIRD is proven to combine the advantages of multiple online pricing strategies to achieve the performance close to the posterior optimal strategy for any sequence of online buyers on realistic sales platforms with inventory limitation. BIRD is proved to yield a vanishing regret for the online posted pricing problem with the features of limited inventory and multi-unit demand. Based on the historical data provided by COSCO, one of the largest liner enterprises in the world, we experimentally demonstrate the effectiveness of the proposed algorithm.