Supply Chain Optimization

Latest papers

19 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Learning-Augmented Optimization for Strategic Two-Echelon Spare Parts Network Design
Sep 11, 2026Donato Maragno, Marco Caserta, Alberto Sinigaglia +3Surrogate-Assisted Optimization Metaheuristic Optimization
Amazon RME · IE University, Department of Operations · Reinforcement Learning Research Lab, UniPD
OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Items
Sep 1, 2026Shuze Daniel Liu, David Simchi-Levi, Claire Chen +2Inventory Control Transformer
Massachusetts Institute of Technology · Purdue University · California Institute of Technology +1
A Graph Neural Network--Guided Genetic Algorithm for Physical Internet Supply Chain Optimization under Cost Uncertainty
Aug 10, 2026Faezeh Ardali, Gerald M. KnappGraph Neural Networks Evolutionary Optimization
Department of Mechanical and Industrial Engineering Louisiana State University, Baton Rouge, LA, USA
Per-Shipment Multi-Agent Reinforcement Learning for Intermodal Freight Routing Under Hurricane Disruption
Aug 7, 2026Aliza Sharmin, Xudong Wang, Mustafa Can Camur +1Vehicle Routing Proximal Policy Optimization
Department of Industrial and Systems Engineering University of Tennessee Knoxville, TN 37996
SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination
Jul 30, 2026Yunhao Liang, Xianqi Cao, Pujun Zhang +4Inventory Control Supply Chain Management
The University of Hong Kong, Hong Kong SAR, China · OptiMax AI, Hong Kong SAR, China · JD.com, China
SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning
Jul 23, 2026Jiayin He, Yutong Pan, Sen Yang +5Combinatorial Optimization Model-Based Planning
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China · JD.com, Beijing, China, 101111 · College of Engineering, UC Berkeley, Berkeley, CA 94720, USA +1
On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions
Jul 18, 2026Tushar Lone, Neha KaranjkarNeural Surrogate Modeling Topology Optimization
School of Mathematics and Computer Science, Indian Institute of Technology Goa, Ponda, Goa, INDIA
A Policy Decomposition Framework for Dynamic Order Fulfillment Operations
Jul 4, 2026Gal Neria, Michal Tzur, Marlin W. UlmerMetaheuristic Optimization Supply Chain Management
Massachusetts Institute of Technology, Cambridge, 02139, MA, USA · School of Industrial and Intelligent Systems Engineering, Tel Aviv University, Tel Aviv, 6997801, Israel · Otto-von-Guericke Universitat Magdeburg, Magdeburg, Germany
Solver-Verified Formulation Generation and Selection for Multi-Warehouse Inventory Allocation Using Large Language Models
Jun 28, 2026Jintao Xu, Yingzheng Ma, Jiong Dong +4Integer Linear Programming Mixed-Integer Linear Programming
Supply Chain Tech Team Y, JD.com
Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application
Jun 24, 2026Tinghan Ye, Shuaicheng Tong, Changkun Guan +2Stochastic Optimization Learning to Optimize
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 · Tippie College of Business, University of Iowa, Iowa City, IA 52242
Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation
Jun 20, 2026Rifny Rachman, Bahrul Ilmi Nasution, Josh Tingey +3Multi-Objective Reinforcement Learning Multi-Objective Evolutionary Optimization
The University of Manchester, United Kingdom · Peak AI, Ltd, United Kingdom
ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience
Jun 9, 2026Jia LuoConstrained RL LLM-Guided RL
School of Foreign Languages, Huazhong University of Science and Technology, Wuhan 430074, China.
Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management
Jun 6, 2026Gerçek Budak, Faraz Gholamzadeh Gharehgheshlaghi, Melika Barjesteh Vaezi +1Time Series Forecasting Combinatorial Optimization
Department of Industrial Engineering, Ankara Yıldırım Beyazıt University, Keçiören, Ankara 06010, Türkiye · Department of Kinesiology and Sport Management, Texas Tech University, Lubbock, TX, United States · Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
Optimal and Order-optimal Gated Priority-based Greedy Policies for Two-layer Multi-item Order Fulfillment
May 25, 2026Xi Chen, Yuze Chen, Ziyi Chen +1Inventory Control Competitive Analysis
Leonard N. Stern School of Business, New York University, New York, NY 10012, USA · Qiuzhen College, Tsinghua University, Beijing 100084, China · Yau Mathematical Sciences Center & Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China
Online Allocation with Unknown Shared Supply
May 8, 2026Tzeh Yuan Neoh, Davin Choo, Mengchu Yue +1Supply Chain Management Sequential Decision Making
Harvard University
Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization
Apr 23, 2026Nusrat Yasmin Nadia, Md Habibul Arif, Habibor Rahman Rabby +3Time Series Forecasting Supply Chain Optimization
Department of Information Technology, Washington University of Science and Technology, , Alexandria, 22314, VA, USA · Department of Information Technology, University of the Potomac, , Washington, D.C., 20005, DC, USA · Department of Computer Science, Campbellsville University, , Louisville, 40220, KY, USA +2
Bridging Deep Learning and Integer Linear Programming: A Predictive-to-Prescriptive Framework for Supply Chain Analytics
Apr 2, 2026Khai Banh Nghiep, Duc Nguyen Minh, Lan Hoang ThiSupply Chain Optimization Supply Chain Management
Tran Dai Nghia Secondary and High School, Vietnam · AI engineer Vietnam · University of Languages and International studies
MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation
Mar 5, 2026Rifny Rachman, Josh Tingey, Richard Allmendinger +3Supply Chain Optimization Multi-Objective Reinforcement Learning
The University of Manchester, United Kingdom · Peak AI, Ltd, United Kingdom
OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents
Feb 23, 2026Ruicheng Ao, David Simchi-Levi, Xinshang WangSupply Chain Optimization Large Language Model-Guided Optimization
Massachusetts Institute of Technology. · Alibaba Group.

Sep 11, 2026J/K move · Enter open · S save

Learning-Augmented Optimization for Strategic Two-Echelon Spare Parts Network Design

Donato Maragno, Marco Caserta, Alberto Sinigaglia, Komlanvi Ametana+2

Amazon RME · IE University, Department of Operations · Reinforcement Learning Research Lab, UniPD

Surrogate-Assisted Optimization Metaheuristic Optimization Supply Chain Management Learning-Augmented Algorithms Supply Chain Optimization

Open paper AskPDF arXiv

We study the strategic design of a two-echelon spare-parts inventory network where evaluating each candidate topology requires an expensive inventory optimization model. The design partitions hundreds of sites into feasible clusters and selects a central replenishment site for each cluster to reduce costs while maintaining service levels. Because the optimizer favors candidates with high predicted savings, it can exploit optimistic surrogate errors. We develop a conservative framework combining a graph neural network ensemble, variable neighborhood search, and set-partitioning recombination. The surrogate is trained on exact cluster evaluations, while a lower quantile of ensemble-predicted savings guides the search to limit optimism. Clusters found during the search are recombined through set partitioning using surrogate-based objective coefficients. The resulting network is evaluated with the exact inventory model, and only this evaluation is used to report performance. In a case study of 246 fulfillment centers in Amazon's North American network, the framework improves combined savings by 30.5% over an optimization baseline based entirely on exact cluster evaluations, while maintaining approximately 99.8% service across six independent replications. Under equal computational budgets, graph-surrogate-guided search achieves higher mean exact savings than a tabular alternative under both scoring schemes. Conservative scoring improves mean savings for both surrogate classes and reduces the share of final-network clusters overestimated by the graph surrogate from 68% to 28%. Predictive and ranking accuracy deteriorate among search-generated candidates with high surrogate scores, indicating that random holdout performance can incompletely characterize surrogate quality during optimization.