Quantifying the uncertainty of machine learning models is crucial for numerous applications, particularly in large-scale real-world scenarios where prediction sets, rather than point predictions, enable more flexible and informed decision making. Uncertainty quantification (UQ) methods are essential for characterizing the unpredictibility arising in various real-world problems across science and engineering. Initially proposed by Vovk et al. [2005], CP is a popular distribution-free method for UQ, largely due to its ability to provide finite-sample coverage guarantees and its computational efficiency. Most studies in CP focus on constructing prediction intervals based on a fitted mean model. This work introduces a novel setting where the value of interest is estimated using only a pair of valid upper and lower bounds, instead of a mean model. While valid bounds themselves provide perfect coverage by definition, they can sometimes be overly conservative. By slightly reducing the coverage level, these bounds can be tightened, resulting in significantly smaller intervals with theoretical guarantees and greater utility for decision making.

This study develops a deep learning-based approach to automate inbound load plan adjustments for a large transportation and logistics company. It addresses a critical challenge for the efficient and resilient planning of E-commerce operations in presence of increasing uncertainties. The paper introduces an innovative data-driven approach to inbound load planning. Leveraging extensive historical data, the paper presents a two-stage decision-making process using deep learning and conformal prediction to provide scalable, accurate, and confidence-aware solutions. The first stage of the prediction is dedicated to tactical load-planning, while the second stage is dedicated to the operational planning, incorporating the latest available data to refine the decisions at the finest granularity. Extensive experiments compare traditional machine learning models and deep learning methods. They highlight the importance and effectiveness of the embedding layers for enhancing the performance of deep learning models. Furthermore, the results emphasize the efficacy of conformal prediction to provide confidence-aware prediction sets. The findings suggest that data-driven methods can substantially improve decision making in inbound load planning, offering planners a comprehensive, trustworthy, and real-time framework to make decisions. The initial deployment in the industry setting indicates a high accuracy of the proposed framework.