Optimal Transport for Supply Chain Resilience 2026
Optimal Transport for Supply Chain Resilience
Mathematical programming, AI-driven optimization, and network-theoretic approaches are converging to build disruption-resilient supply chains. Patent and literature signals spanning 2014–2026 reveal where this field is heading.
From Theoretical Models to Deployable AI Systems
Supply chain resilience optimization spans mathematical programming, network flow analysis, AI-driven decision systems, and simulation-based assessment. Among the 80+ records retrieved in this dataset, the heaviest publication activity falls in 2021–2023, coinciding with post-COVID-19 supply chain stress and accelerated digitalization.
The field has evolved through three phases: a foundational phase (2014–2018) establishing resilience as a measurable, optimizable property; an acceleration phase (2019–2021) where robust stochastic programming became standard; and a deployment and digitalization phase (2022–2026) dominated by AI, digital twins, and Industry 4.0 integration.
Key sub-domains include resilience quantification and indexing, stochastic and robust optimization models using MILP and scenario-based programming, network topology and flow analysis, AI/ML-augmented decision systems including reinforcement learning and digital twins, and sustainability-resilience co-optimization using multi-objective programming.
In this dataset, patent filing activity is concentrated in a single assignee — IBM holds 3 filings spanning 2022–2026 — while academic literature innovation is distributed across dozens of institutions globally, with China-based empirical studies representing the most frequent geographic context in retrieved records.
Filing Timeline and Technology Cluster Breakdown
Patent filings in this dataset cluster around IBM’s 2022–2026 US filings and an emerging 2025 CN filing from Nanjing Xiaozhuang College. Literature publication volume accelerated sharply after 2019, with the systematic review noting 94 papers published in 2017–2019 versus only 47 between 2007 and 2016.
Patent Assignees by Filing Count — Retrieved Records (Dataset Snapshot)
IBM accounts for 3 of the 5 total patent records in this dataset, with the remaining 2 filings from Nanjing Xiaozhuang College and Dr. R. Bhuvana respectively.
↗ Click bars to exploreLiterature Publication Volume by Phase — Optimal Transport Supply Chain Resilience
Publication volume in this dataset grew from 47 papers (2007–2016) to 94 papers (2017–2019), with a further surge post-2020 driven by COVID-19 empirical validation studies and digitalization research.
↗ Click bars to exploreKey Deployment Contexts for Supply Chain Resilience Optimization
Optimal transport and resilience optimization methods have been applied across diverse sectors and geographies in this dataset, from Nigerian gas pipeline networks to Chinese highway freight systems and global food trade networks.
Nigerian Natural Gas Network
An integrated MILP model applied to a real Nigerian gas infrastructure network yielded a 93.6% performance improvement through contingency pipeline design. The 2022 study jointly optimizes resilience and sustainability objectives. This represents one of the most quantitatively validated applications in the dataset.
Energy & Natural ResourcesJD.COM E-Commerce Supply Chain
JD.COM deployed a digital twin-driven smart supply chain platform during COVID-19 disruption, enabling real-time network reconfiguration. A 2021 case study also documented JD.COM’s integrated use of digital platforms and delivery modification to strengthen resilience. These studies represent the most detailed e-commerce deployment examples in the dataset.
E-Commerce & RetailTanzania Road Transport Network
A 2020 agent-based supply chain model simulated firm behavior under transport-supply disruptions across Tanzanian road infrastructure, generating criticality maps that identified non-linear loss scaling at specific nodes. The study demonstrates how physical transport network topology directly shapes supply chain resilience outcomes and food security impacts.
Food & AgricultureGlobal Food Trade Network
A 2021 study applied information theory-based network flow analysis to 50 years of global commodity trade data, quantifying the efficiency-resilience tradeoff in food trade networks. The complex network framework provides a direct application of optimal transport concepts to international food supply systems, revealing structural vulnerabilities at scale.
Food & AgricultureKey Patent Assignees in Optimal Transport Supply Chain Resilience (Retrieved Records)
In this dataset, IBM accounts for 3 of the 5 total patent records, all directed at AI-driven software systems for supply chain resilience optimization filed between 2022 and 2026 in the US jurisdiction. Nanjing Xiaozhuang College filed 1 pending CN patent in 2025, representing emerging academic patent activity in Asia in retrieved records.
Assignee Filing Counts — Optimal Transport Supply Chain Resilience (Dataset Snapshot)
↗ Click bars to exploreInternational Business Machines Corporation
IBM holds 3 patent records in this dataset spanning 2022–2026, all active US filings directed at AI-driven supply chain resilience software systems. Key patents include a 2023 core system using spatio-temporal climate forecasts and reasoning graphs for dynamic policy generation, a 2026 extension adding cluster-level Pareto optimization across upstream-downstream nodes, and a 2022 patent applying deep reinforcement learning and Gaussian Process Regression with joint carbon footprint and risk optimization. Two filings are active; one is inactive.
United StatesNanjing Xiaozhuang College
Nanjing Xiaozhuang College filed 1 pending CN patent in July 2025, covering a multi-objective supply chain resilience optimization method using a hybrid simulation-reinforcement learning framework. The patent introduces a composite Supplier Stability Index (SSI) incorporating political, public opinion, operational, and accident data, and targets a cost-service-resilience balanced objective function. This is the most recent patent in the dataset and signals emerging academic-to-patent translation in China.
China — CNFive Forward-Looking Signals from 2023–2026 Records
The most recent filings and publications in this dataset (2023–2026) point to five identifiable forward-looking directions, ranging from fully automated network-wide AI re-optimization to multi-risk long-horizon resilience assessment frameworks.
Spatio-Temporal AI with Feedback Loops
The 2026 IBM patent extends AI-driven systems to include Pareto optimization across node clusters with real-time upstream-downstream communication. This signals movement toward fully automated, network-wide resilience re-optimization triggered by climate and operational signals. The system uses joint ML model optimization with temporal feedback loops across supply chain nodes.
Simulation-Reinforcement Learning Hybrids
The 2025 CN patent from Nanjing Xiaozhuang College introduces a hybrid simulation-reinforcement learning framework that trains emergency strategies in virtual environments. It combines political, public opinion, operational, and accident data into a composite Supplier Stability Index (SSI). This cost-service-resilience balanced objective function approach represents a frontier in deployable multi-objective optimal transport.
Mathematical Programming vs. AI-Driven Optimization for Supply Chain Resilience
Click any row to explore further.
| Dimension | Mathematical Programming (MILP/Stochastic) | AI/ML-Driven Optimization (RL/Deep Learning) |
|---|---|---|
| Primary Approach | Mixed-integer linear programming, stochastic/robust optimization, scenario-based programming | Reinforcement learning, deep learning, Gaussian Process Regression, spatio-temporal feedback |
| Objective Structure | Multi-objective: cost minimization, social and environmental supplier scores (ε-constraint, goal programming) | Multi-objective: resilience + carbon footprint + risk jointly optimized via ML model |
| Key Example from Dataset | 93.6% performance improvement via contingency pipeline design in Nigerian gas network (2022 MILP study) | IBM 2023 US patent: spatio-temporal climate forecasts drive dynamic resiliency policy via reasoning graphs |
| Maturity Phase | Acceleration phase (2019–2021); became standard in literature by post-COVID period | Deployment and digitalization phase (2022–2026); most patent-intensive cluster in this dataset |
| Patent Activity in Dataset | Primarily in academic literature; limited direct patent protection identified in this dataset | 3 active/pending US patents from IBM (2022–2026) covering deployable software systems |
| Real-Time Capability | Generally offline/batch; proactive-reactive combined strategies using LHS and SAA methods | Real-time policy updates via temporal feedback loops and upstream-downstream inter-node communication |
| Carbon/Sustainability Integration | Three-objective robust stochastic model minimizing cost, maximizing social and environmental scores | IBM ML patent explicitly integrates carbon footprint estimation into resilience plan generation loop |
| Whitespace Opportunity”> | Transport criticality mapping methods remain primarily in literature with limited patent protection | Reinforcement learning for supply chain recovery has limited prior art in this dataset outside IBM |
Frequently Asked Questions: Optimal Transport for Supply Chain Resilience
Mathematical programming models — including mixed-integer linear programming (MILP), stochastic and robust optimization, and multi-objective formulations — form the dominant technical cluster in this dataset. These models optimize facility location, supplier selection, inventory positioning, and flow allocation under disruption scenarios.
International Business Machines Corporation (IBM) holds 3 patent records in this dataset spanning 2022–2026, all active US filings. These cover spatio-temporal feedback-driven resiliency systems, cluster-level Pareto optimization, and deep reinforcement learning with carbon footprint integration. No other assignee has more than 1 filing in the retrieved records.
The field has evolved through three phases: a Foundational Phase (2014–2018) establishing resilience as a measurable, optimizable property; an Acceleration Phase (2019–2021) where robust stochastic programming became standard and publication volume nearly doubled (94 papers in 2017–2019 vs. 47 in 2007–2016); and a Deployment and Digitalization Phase (2022–2026) dominated by AI, digital twins, and Industry 4.0 integration.
The 2025 CN patent from Nanjing Xiaozhuang College introduces a composite Supplier Stability Index (SSI) that combines political, public opinion, operational, and accident data into a single metric. It is used within a hybrid simulation-reinforcement learning framework to train emergency strategies in virtual environments, targeting a cost-service-resilience balanced objective function.
Application domains identified in this dataset include energy and natural resources (Nigerian gas infrastructure, oil and gas), food and agriculture (global food trade, dairy, biomass), logistics and container transport (Chinese highway freight, container network flow), humanitarian and emergency response (two-stage network DEA models, big data analytics), manufacturing and aerospace (large passenger aircraft, steel industry), and e-commerce (JD.COM digital twin deployment).
Two potential whitespace areas are identified: first, reinforcement learning applied to supply chain recovery strategy generation has limited prior art outside IBM’s filings in this dataset, representing a potential opportunity for non-IBM filers in US and EP jurisdictions. Second, transport network criticality mapping methods — including agent-based and graph-theoretic approaches for infrastructure resilience assessment — remain primarily in academic literature with limited patent protection in this dataset.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.