Supply Chain Management

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3 papers in the last four weeks, down 73% on the four weeks before. 0.0% of all new papers.

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Reducing Complexity for Quantum Approaches in Train Load Optimization
Mar 31, 2026Zhijie Tang, Albert Nieto-Morales, Arit Kumar BishwasCombinatorial Optimization Supply Chain Management
Commercial Technology and Innovation, Shanghai, China · Tech and Innovation, San Francisco, US
Learning to Staff: Offline Reinforcement Learning and Fine-Tuned LLMs for Warehouse Staffing Optimization
Mar 25, 2026Kalle Kujanpää, Yuying Zhu, Kristina Klinkner +1LLM Decision-Making Supply Chain Management
Amazon, Fulfillment Technologies & Robotics
The Economics of AI Supply Chain Regulation
Mar 13, 2026Sihan Qian, Amit Mehra, Dengpan LiuAI Governance Supply Chain Management
School of Economics and Management, Tsinghua University. · Naveen Jindal School of Management, The University of Texas at Dallas.
LLM-Grounded Explainable AI for Supply Chain Risk Early Warning via Temporal Graph Attention Networks
Mar 5, 2026Zhiming Xue, Yujue Wang, Menghao HuoSupply Chain Management LLM Reasoning with Graphs
College of Engineering, Northeastern University, Boston, USA · Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, USA · School of Engineering, Santa Clara University, Santa Clara, USA
Capability Thresholds and Manufacturing Topology: How Embodied Intelligence Triggers Phase Transitions in Economic Geography
Mar 3, 2026Xinmin Fang, Lingfeng Tao, Zhengxiong LiManufacturing Embodied AI
LeTau Robotics · University of Colorado Denver · Kennesaw State University
Coverage-Aware Web Crawling for Domain-Specific Supplier Discovery via a Web--Knowledge--Web Pipeline
Feb 27, 2026Yijiashun Qi, Yijiazhen Qi, Tanmay WaghSupply Chain Management KG Construction
University of Michigan Ann Arbor, USA · The University of Hong Kong Hong Kong, China · Santa Clara University Santa Clara, USA
Multi-Objective Reinforcement Learning for Large-Scale Tote Allocation in Human-Robot Collaborative Fulfillment Centers
Feb 27, 2026Sikata Sengupta, Guangyi Liu, Omer Gottesman +4Multi-Objective Reinforcement Learning Supply Chain Management
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA · Amazon.
Sharing is caring: data sharing in multi-agent supply chains
Feb 27, 2026Wan Wang, Haiyan Wang, Adam SobeyMulti-Agent Systems Supply Chain Management
School of Transportation and Logistics Engineering, Wuhan University of Technology, No.1178 Heping Avenue Wuchang District, Wuhan, 430063, Hubei, China · Maritime Engineering, University of Southampton, Burgess Rd, Southampton, SO17 1BJ, UK · Sustainability Mission, The Alan Turing Institute, The British Library, London, NW1 2DB, UK
Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning
Feb 23, 2026Stefan Faulkner, Reza Zandehshahvar, Vahid Eghbal Akhlaghi +3Supply Chain Management Multi-Task Learning
NSF Artificial Intelligence Institute for Advances in Optimization, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA · Kinaxis
Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models
Feb 16, 2026Pavel Koptev, Vishnu Kumar, Konstantin Malkov +2Financial Services Financial Forecasting
AI1 Technologies · The Interface Financial Group

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Reducing Complexity for Quantum Approaches in Train Load Optimization

Zhijie Tang, Albert Nieto-Morales, Arit Kumar Bishwas

Commercial Technology and Innovation, Shanghai, China · Tech and Innovation, San Francisco, US

Combinatorial Optimization Supply Chain Management

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Efficiently planning container loads onto trains is a computationally challenging combinatorial optimization problem, central to logistics and supply chain management. A primary source of this complexity arises from the need to model and reduce rehandle operations-unproductive crane moves required to access blocked containers. Conventional mathematical formulations address this by introducing explicit binary variables and a web of logical constraints for each potential rehandle, resulting in large-scale models that are difficult to solve. This paper presents a fundamental departure from this paradigm. We introduce an innovative and compact mathematical formulation for the Train Load Optimization (TLO) problem where the rehandle cost is calculated implicitly within the objective function. This novel approach helps prevent the need for dedicated rehandle variables and their associated constraints, leading to a dramatic reduction in model size. We provide a formal comparison against a conventional model to analytically demonstrate the significant reduction in the number of variables and constraints. The efficacy of our compact formulation is assessed through a simulated annealing metaheuristic, which finds high-quality loading plans for various problem instances. The results confirm that our model is not only more parsimonious but also practically effective, offering a scalable and powerful tool for modern rail logistics.