Coupon distribution is a critical marketing strategy used by online platforms to boost revenue and enhance user engagement. Regrettably, existing coupon distribution strategies fall far short of effectively leveraging the complex sequential interactions between platforms and users. This critical oversight, despite the abundance of e-commerce log data, has precipitated a performance plateau. In this paper, we focus on the scene that the platforms make sequential coupon distribution decision multiple times for various users, with each user interacting with the platform repeatedly. Based on this marketing scenario, we propose a novel marketing framework, named Sequence-Aware Constrained Optimization (SACO) framework, to directly devise coupon distribution policy for long-term revenue boosting. SACO framework enables optimized online decision-making in a variety of real-world marketing scenarios. It achieves this by seamlessly integrating three key characteristics, general scenarios, sequential modeling with more comprehensive historical data, and efficient iterative updates within a unified framework. Furthermore, empirical results on real-world industrial dataset, alongside public and synthetic datasets demonstrate the superiority of our framework.

Additive manufacturing (AM) or 3-D printing refers to a new class of technologies that actively construct products directly from any 3-D digital model. In the future, the broader applications of AM will require a cost reduction of AM machines. Currently, the products fabricated by low-end machines, such as those fabricated using fused deposition modeling (FDM) processes, suffer from the issue of low dimensional accuracy due to multiple error sources. To properly manage error sources for improved prevision, this paper proposes a novel strategy for error compensation in the FDM processes. First, we consecutively attribute the dimensional inaccuracy to two major error sources that affect the geometric shape of the product: 1) positioning error of the extruder and 2) shape deformation induced by processing error, including material phase change and other variations that occur. The extruder positioning error is characterized by a Kriging model, while the modeling of shape deformation due to processing error follows the method developed by Huang et al. Second, using error equivalence concept, we transform the positioning error into the equivalent amount of design input error. Finally, we adjust the design to compensate for the overall shape deviation. To validate this strategy, we conduct a designed experiment for the shape deviation prediction and the compensation. The experimental results successfully demonstrate the effectiveness of the proposed three-step strategy to manage multiple error sources in the FDM processes.