8 papers
Variable-Horizon Workforce Demand Forecasting with an Aggregate Demand Constraint for Construction Workforce Planning
Hanbyeol Park, Hanbyeol park, Jaehyeon Heo +3
Workforce planning is a recurring operational decision during construction projects that requires accurate forecasts of the future workforce demand for individual tasks. However, i…
Trie-Constrained Token Prediction with Hierarchy-Aware Semantic Alignment for HS Code Prediction
Minseop Kim, Taekhyun Park, Kikun Park +1
Harmonized System (HS) code prediction (HSP) from commodity text is essential to international trade, and its importance continues to grow in port logistics. For the purposes of su…
Rewarding Structural Conformance of Reasoning using Process Mining
Yongjae Lee, Taekhyun Park, Sunghyun Sim +1
Recent advances in sparse reward policy gradient methods have enabled effective reinforcement learning (RL)-based language model post-training. However, for reasoning tasks such as…
LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models
Taekhyun Park, Yongjae Lee, Dohee Kim +1
Looped computation shows promise in improving the reasoning-oriented performance of LLMs by scaling test-time compute. However, existing approaches typically require either trainin…
Generative AI and Machine Learning Collaboration for Container Dwell Time Prediction via Data Standardization
Minseop Kim, Takhyeong Kim, Taekhyun Park +2
Import container dwell time (ICDT) prediction is a key task for improving productivity in container terminals, as accurate predictions enable the reduction of container re-handling…
Application of Large Language Models for Container Throughput Forecasting: Incorporating Contextual Information in Port Logistics
Minseop Kim, Jaeeun Kwon, Hanbyeol Park +3
Recent advancements in generative artificial intelligence (AI) have demonstrated its substantial potential in various fields. However, its application in port logistics remains und…