Supply Chain Optimization

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Period ending 2026-09-21

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Supply Chain Optimization

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Last Mile

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Monotonic Inventory Estimation

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Warehouse Automation

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Dynamic Order Fulfillment Operations

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154 papers

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Learning Selective Merge Policies for Deadline-Constrained Coded Caching via Deep Reinforcement Learning
May 13, 2026Amirhossein YousefiramandiWireless Resource Allocation Dynamic Order Fulfillment Operations
An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing
May 13, 2026Hanwen Zhang, Dusit Niyato, Wei Zhang +2Edge Computing Last Mile
GenAI-Driven Approach to RISC-V Supply Chain Exploration
May 13, 2026Nenad Petrovic, Andre Schamschurko, Yingjie Xu +1Supply Chain Optimization Risc-V
Conveyor Parcel Routing with Order-Contiguous Arrivals
May 13, 2026Takuro Kato, Keisuke OkumuraLast Mile Multi-Agent Path Finding
Graph-Driven Cross-Industry Real-Time Monitoring Framework for Anti-Money Laundering Detection in Converged Mobility-Energy Supply Chain Networks
May 13, 2026Rong Liu, Xiaojun Xiao, Zhanqing SuAnti-Money Laundering Supply Chain Optimization
ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks
May 12, 2026Zhizhen Zhang, Hyemin Gu, Benjamin J. Zhang +6Supply Chain Optimization Time Series
gym-invmgmt: An Open Benchmarking Framework for Inventory Management Methods
May 12, 2026Reza Barati, Qinmin Vivian HuMonotonic Inventory Estimation Reinforcement-Learning Policies
Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems
May 9, 2026Jiaxin Liu, Peng Yang, Yuping Li +1Dynamic Order Fulfillment Operations Last Mile
Many-to-Many Multi-Agent Pickup and Delivery
May 8, 2026Ethan Schneider, Jingkai Chen, Tianyi Gu +3Warehouse Automation Multi-Robot Systems
Intelligent Truck Matching in Full Truckload Shipments using Ping2Hex approach
May 8, 2026Srinivas Kumar Ramdas, Jose Mathew, Ankit Singh Chauhan +3Last Mile Onboard Global Navigation Satellite System Ground Truth
Business Utility of Large Language Models as Exploratory Data Analysis Agents
May 8, 2026Rafał Łabędzki, Patryk Miziuła, Hubert Rutkowski +5Reproducibility Supply Chain Optimization
Online Allocation with Unknown Shared Supply
May 8, 2026Tzeh Yuan Neoh, Davin Choo, Mengchu Yue +1Monotonic Inventory Estimation Supply Chain Optimization
SOAR: Real-Time Joint Optimization of Order Allocation and Robot Scheduling in Robotic Mobile Fulfillment Systems
May 5, 2026Yibang Tang, Yifan Yang, Jingyuan Wang +2Dynamic Order Fulfillment Operations Optimal Scheduling
AI-Driven Controlled Environment Agriculture as Resilient Infrastructure for U.S. Fresh-Produce Supply Chains
May 4, 2026Andrii VakhnovskyiMediterranean Greenhouses Supply Chain Optimization
Middle-mile logistics through the lens of goal-conditioned reinforcement learning
May 4, 2026Onno Eberhard, Thibaut Cuvelier, Michal Valko +1Goal-Conditioned Reinforcement Learning Last Mile
Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget
May 2, 2026Yi Yang, Jinyang Huang, Binbin Liu +5Backdoor Attacks Noisy Labels
LLM Ghostbusters: Surgical Package Hallucination Suppression via Adaptive Unlearning
May 1, 2026Joseph Spracklen, Pedram Aghazadeh, Farinaz Koushanfar +1Large Language Model Hallucination Language Model Evasion Attacks
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
May 1, 2026Chenyu Huang, Jianghao Lin, Zhengyang Tang +4Monotonic Inventory Estimation Alphaevolve
Framework for Collaborative Operation of Autonomous Delivery Vehicles Within a Marshaling Yard
Apr 30, 2026James O'Hara, Karl Wunderlich, Gregory StevensAutonomous Driving Last Mile
Learning to Route Electric Trucks Under Operational Uncertainty
Apr 29, 2026Stavros Orfanoudakis, Ziyan Li, Ruixiao Yang +4Electric Vehicles Vehicle Routing Problem
Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization
Apr 23, 2026Nusrat Yasmin Nadia, Md Habibul Arif, Habibor Rahman Rabby +3Demand Forecasting Supply Chain Optimization
Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration
Apr 23, 2026Simon Jarvers, Orestis PapakyriakopoulosArtificial Intelligence Governance Team Formation
Integrated packing, placement, scheduling, and routing of personalized production: a pharmaceutical Industry 4.0 use-case with a planar transport system
Apr 22, 2026Viktor Emil Korladinov, Antonin Novak, Zdeněk Hanzálek +2Industrial Automation Last Mile
Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation
Apr 19, 2026Jiuyun Jiang, Yuecheng Hong, Bo Yang +4Multi-Agent Simulations Supply Chain Optimization
Vision-Based Human Awareness Estimation for Enhanced Safety and Efficiency of AMRs in Industrial Warehouses
Apr 18, 2026Maximilian Haug, Christian Stippel, Lukas Pscherer +5Human-Robot Collaboration Robotic Perception
R&F-Inventory: A Large-Scale Dataset for Monotonic Inventory Estimation in Reach and Frequency Advertising
Apr 18, 2026Yunshan Peng, Ji Wu, Wentao Bai +6Ads Monotonic Inventory Estimation
Towards Predicting Multi-Vulnerability Attack Chains in Software Supply Chains from Software Bill of Materials Graphs
Apr 4, 2026Laura Baird, Armin MoinVulnerability Detection & Repair Heterogeneous Graph Transformers
Adaptive Automatic Model Selection for Demand Forecasting under Heterogeneous Demand Patterns
Feb 15, 2026Adolfo González, Víctor ParadaDemand Forecasting Model Selection
Parametric and Generative Forecasts of EPEX Day\char45 Ahead Energy Market Curves
Jan 28, 2026Julian Gutierrez, Redouane SilventeElectricity Price Forecasting Stochastic Volatility
IndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation
Nov 21, 2025Yifan Li, Lichi Li, Anh Dao +15Vision-Language Navigation Spatial Reasoning
HERB: Human-augmented Efficient Reinforcement learning for Bin-packing
Apr 23, 2025Gojko Perovic, Nuno Ferreira Duarte, Atabak Dehban +3Scalable Robot Learning Warehouse Automation
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planning
Feb 18, 2025Jaike van Twiller, Yossiri Adulyasak, Erick Delage +2Deep Reinforcement Learning Last Mile
Combinatorial Inference on the Optimal Assortment in Multinomial Logit Models
Jan 28, 2023Shuting Shen, Xi Chen, Ethan X. Fang +1Semiparametric Framework Generalized Linear Bandit
Dynamic Multi-Agent Pickup and Delivery in Robotic Cellular Warehousing Systems
Date pendingCheng Ren, Ming Li, Xinping Guan +1Multi-Robot Systems Warehouse Automation

May 13, 2026 · cs.ITJ/K move · Enter open · S save

Learning Selective Merge Policies for Deadline-Constrained Coded Caching via Deep Reinforcement Learning

Amirhossein Yousefiramandi

In the coded caching, the server uses the cached information at the users to serve multiple users in parallel with a single coded multi-casting message or packet, that is, a merged packet, and thus mitigates the peak network congestion. In order to deliver the timely messages to the users in the deadline-driven applications like the video streaming, we must determine online the messages to be merged for the delivery, as there is a time limit for each request. It is important to note that while the merging aids the current coded multi-casting packet, it could harm the future deliveries. Our solution employs the deep reinforcement learning to view the coded multi-casting delivery as a masked action-discrete state control problem, and our policy network, trained via the proximal policy optimization, performs better than SACM++. On the uniform-demand benchmark, our policy network reduces the broadcast-packet expiration ratio

ρ

40.9\%

(

0.208

vs.\

0.352

) with respect to the best coded multi-casting baseline (SACM++), while also attaining the best broadcast-efficiency score

σ

across the Track~A battery among the coded multi-casting methods. One noteworthy phenomenon here is that, for the applications with stricter deadlines, the merging becomes selective instead of aggressive, since the policy network selectively merges at approximately

31.8\%

of the chances, even though the same observation holds across the variations within the same simulator family. The focus of our design is on the efficient pairwise XOR merging, where the higher-order (

K{\ge}3

) coding can be considered as a natural generalization left for future work.

Wireless Resource Allocation Dynamic Order Fulfillment Operations Continual Model Merging Deep Reinforcement Learning

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