arXiv AI

SupplyNetPy: An Open-Source Python Library for High-Fidelity Modeling and Simulation of Arbitrary Supply Chain and Inventory Networks

arXiv:2607. 09745v1 Announce Type: new Abstract: This paper introduces SupplyNetPy, an open-source, well-documented Python library for modeling and discrete-event simulation of supply chain networks with arbitrary multi-echelon structures.

arXiv Machine Learning

Jul 21

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions

arXiv:2607. 16769v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models for graph-structured systems.

By Tushar Lone, Neha Karanjkar

Hugging Face Trending Papers

Jul 23

SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning

For years, supply chain planning at e-commerce firms has operated as a collection of isolated projects. Each planning task from static network planning to dynamic warehouse assortment planning requires analysts to spend weeks building models from scratch, calibrating and persuading executives to act on outputs they cannot verify.

arXiv AI

Jul 24

SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning

arXiv:2607. 21354v1 Announce Type: new Abstract: For years, supply chain planning at e-commerce firms has operated as a collection of isolated projects.

By Jiayin He, Yutong Pan, Sen Yang, Ningxuan Kang, Yongzhi Qi, Jianshen Zhang, Wei Qi, Zuo-Jun Max Shen

arXiv Machine Learning

Jun 2

ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks

arXiv:2605. 12768v2 Announce Type: replace-cross Abstract: Open time-series forecasting (TSF) benchmarks cover retail, energy, weather, and traffic, but supply-chain logistics remains underserved.

By Zhizhen Zhang, Hyemin Gu, Benjamin J. Zhang, Daniel Elenius, Michael Tyrrell, Theo J. Bourdais, Houman Owhadi, Markos A. Katsoulakis, Tuhin Sahai

llms benchmarks

arXiv Machine Learning

Jul 31

SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination

arXiv:2607. 28488v1 Announce Type: cross Abstract: Can supply-chain AI move beyond isolated decision modules toward unified operational planning?

By Yunhao Liang, Xianqi Cao, Pujun Zhang, Yuan Qu, Yongzhi Qi, Ningxuan Kang, Max Z. J. Shen

arXiv AI

Jun 26

Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization

arXiv:2606. 26852v1 Announce Type: new Abstract: Order fulfillment in manual picker-to-goods warehouses involves interconnected decisions such as item assignment, order batching, and picker routing.

By Janik Bischoff, Anne Meyer, Uta Mohring, Fabian Dunke, Maximilian Barlang, \"Ozge Nur Subas, Hadi Kutabi, Stefan Nickel, Kai Furmans

benchmarks

arXiv Machine Learning

Jul 7

A Policy Decomposition Framework for Dynamic Order Fulfillment Operations

arXiv:2607. 04056v1 Announce Type: cross Abstract: Modern supply chains span diverse operational environments, ranging from e-commerce distribution networks to customized production-to-order manufacturing lines.

By Gal Neria, Michal Tzur, Marlin W. Ulmer

reinforcement-learning benchmarks

arXiv AI

Jul 28

Generative Artificial Intelligence (GenAI) to convert images of queuing networks into verifiable simulation models: an open-weight LLM workflow approach

arXiv:2607. 24259v1 Announce Type: new Abstract: Recent work has explored the use of Large Language Models (LLMs) to automate simulation model building, typically by generating executable code directly from natural language descriptions.

By Thomas Monks, Alison Harper, Amy Heather, Navonil Mustafee

llms multimodal benchmarks

arXiv Machine Learning

Aug 12

A Graph Neural Network--Guided Genetic Algorithm for Physical Internet Supply Chain Optimization under Cost Uncertainty

arXiv:2608. 10245v1 Announce Type: cross Abstract: Inventory and distribution planning in Physical Internet networks requires coordinating factory-hub assignments, factory supply, lateral transshipment among collaborative hubs, retailer deliveries, and shortages.

By Faezeh Ardali, Gerald M. Knapp

benchmarks

arXiv Machine Learning

Jun 19

HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data

arXiv:2507. 22524v3 Announce Type: replace Abstract: We propose HGCN(O), a self-tuning toolkit using Graph Convolutional Network (GCN) models for event sequence prediction.

By Fang Wang, Paolo Ceravolo, Ernesto Damiani

arXiv AI

Jul 22

LLM-Grounded Explainable AI for Supply Chain Risk Early Warning via Temporal Graph Attention Networks

arXiv:2603. 04818v3 Announce Type: replace Abstract: Disruptions at critical logistics nodes pose severe risks to global supply chains, yet existing risk prediction systems typically prioritize forecasting accuracy without providing operationally interpretable early warnings.

By Zhiming Xue, Yujue Wang, Menghao Huo

llms

arXiv Machine Learning

Jun 10

ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling

arXiv:2606. 10440v1 Announce Type: cross Abstract: Distributed machine learning (ML) is a key paradigm for today's large-scale artificial intelligence applications.

By William Won, Jinsun Yoo, Tuan Ta, Moumita Dey, Andy Balogh, Pradosh Datta, Furkan Eris, Conor Green, Winston Liu, Changhai Man, Kingshuk Mandal, Amos Rai, Vinay Ramakrishnaiah, Ruchi Shah, David Sidler, Harsh Sikhwal, Hanjiang Wu, Tushar Krishna, Bradford M. Beckmann

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