Publications

2025

1. MAS & DT

   Multi-Agent Digital Twinning for Collaborative Logistics: Framework and Implementation

   Liming Xu, Stephen Mak, Stefan Schoepf, and 2 more authors

   Journal of Industrial Information Integration, 2025

   Bib HTML

   @article{xu2025multi,
     title = {Multi-Agent Digital Twinning for Collaborative Logistics: Framework and Implementation},
     author = {Xu, Liming and Mak, Stephen and Schoepf, Stefan and Ostroumov, Michael and Brintrup, Alexandra},
     journal = {Journal of Industrial Information Integration},
     volume = {45},
     pages = {100799},
     year = {2025},
     publisher = {Elsevier},
   }

2. VRP

   TuneNSearch: a Hybrid Transfer Learning and Local Search Approach for Solving Vehicle Routing Problems

   Arthur Corrêa, Cristóvão Silva, Liming Xu, and 2 more authors

   arXiv preprint arXiv:2503.12662, 2025

   Bib HTML

   @article{correa2025tunensearch,
     title = {TuneNSearch: a Hybrid Transfer Learning and Local Search Approach for Solving Vehicle Routing Problems},
     author = {Corr{\e}a, Arthur and Silva, Crist{\'o}v{\~a}o and Xu, Liming and Brintrup, Alexandra and Moniz, Samuel},
     journal = {arXiv preprint arXiv:2503.12662},
     year = {2025},
   }

3. PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data

   Yunbo Long, Liming Xu, Ge Zheng, and 1 more author

   arXiv preprint arXiv:2503.12220, 2025
4. Efficient and Privacy-Preserved Link Prediction via Condensed Graphs

   Yunbo Long, Liming Xu, and Alexandra Brintrup

   Expert Systems with Applications, 2025
5. Agentic AI & KG

   Automating Supply Chain Disruption Monitoring Via an Agentic AI Approach

   Sara Almahri, Liming Xu, and Alexandra Brintrup

   Available at SSRN 5317578, 2025

   Bib HTML

   @article{almahri5317578automating,
     title = {Automating Supply Chain Disruption Monitoring Via an Agentic AI Approach},
     author = {Almahri, Sara and Xu, Liming and Brintrup, Alexandra},
     journal = {Available at SSRN 5317578},
     year = {2025},
   }

6. Agentic AI

   A Multi-Level Evaluation Framework for Agentic Systems

   Sara Almahri, Liming Xu, and Alexandra Brintrup

   Submitted for Publication, 2025
7. Syn Data

   LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion

   Yunbo Long, Liming Xu, and Alexandra Brintrup

   arXiv preprint arXiv:2503.02161, 2025

   Abs Bib HTML

   Synthetic tabular data are increasingly being used to replace real data, serving as an effective solution that simultaneously protects privacy and addresses data scarcity. However, in addition to preserving global statistical properties, synthetic datasets must also maintain domain-specific logical consistency**-**especially in complex systems like supply chains, where fields such as shipment dates, locations, and product categories must remain logically consistent for real-world usability. Existing generative models often overlook these inter-column relationships, leading to unreliable synthetic tabular data in real-world applications. To address these challenges, we propose LLM-TabLogic, a novel approach that leverages Large Language Model reasoning to capture and compress the complex logical relationships among tabular columns, while these conditional constraints are passed into a Score-based Diffusion model for data generation in latent space. Through extensive experiments on real-world industrial datasets, we evaluate LLM-TabLogic for column reasoning and data generation, comparing it with five baselines including SMOTE and state-of-the-art generative models. Our results show that LLM-TabLogic demonstrates strong generalization in logical inference, achieving over 90% accuracy on unseen tables. Furthermore, our method outperforms all baselines in data generation by fully preserving inter-column relationships while maintaining the best balance between data fidelity, utility, and privacy. This study presents the first method to effectively preserve inter-column relationships in synthetic tabular data generation without requiring domain knowledge, offering new insights for creating logically consistent real-world tabular data.

   @article{long2025llm,
     title = {LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion},
     author = {Long, Yunbo and Xu, Liming and Brintrup, Alexandra},
     journal = {arXiv preprint arXiv:2503.02161},
     year = {2025},
   }

8. Syn Data

   Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generation

   Yunbo Long, Liming Xu, and Alexandra Brintrup

   arXiv preprint arXiv:2502.04055, 2025

   HTML
9. Syn Data & Graph

   Random Walk Guided Hyperbolic Graph Distillation

   Yunbo Long, Liming Xu, Stefan Schoepf, and 1 more author

   arXiv preprint arXiv:2501.15696, 2025

   Bib HTML

   @article{long2025random,
     title = {Random Walk Guided Hyperbolic Graph Distillation},
     author = {Long, Yunbo and Xu, Liming and Schoepf, Stefan and Brintrup, Alexandra},
     journal = {arXiv preprint arXiv:2501.15696},
     year = {2025},
     abs = {Graph distillation (GD) is an effective approach to extract useful information from large-scale network structures. However, existing methods, which operate in Euclidean space to generate condensed graphs, struggle to capture the inherent tree-like geometry of real-world networks, resulting in distilled graphs with limited task-specific information for downstream tasks. Furthermore, these methods often fail to extract dynamic properties from graphs, which are crucial for understanding information flow and facilitating graph continual learning. This paper presents the Hyperbolic Graph Distillation with Random Walks Optimization (HyDRO), a novel graph distillation approach that leverages hyperbolic embeddings to capture complex geometric patterns and optimize the spectral gap in hyperbolic space. Experiments show that HyDRO demonstrates strong task generalization, consistently outperforming state-of-the-art methods in both node classification and link prediction tasks. HyDRO also effectively preserves graph random walk properties, producing condensed graphs that achieve enhanced performance in continual graph learning. Additionally, HyDRO achieves competitive results on mainstream graph distillation benchmarks, while maintaining a strong balance between privacy and utility, and exhibiting robust resistance to noises.},
   }

10. LLMs in Supply Chain Management: Opportunities and Case Study

    Ge Zheng, Sara Almahri, Liming Xu, and 2 more authors

    IFAC-PapersOnline, 2025
11. CV

    Topological Structure Description for Artcode Detection Using the Shape of Orientation Histogram

    Liming Xu, Dave Towey, Andrew P French, and 1 more author

    arXiv preprint arXiv:2508.10942, 2025

    Bib HTML

    @article{xu2025topological,
      title = {Topological Structure Description for Artcode Detection Using the Shape of Orientation Histogram},
      author = {Xu, Liming and Towey, Dave and French, Andrew P and Benford, Steve},
      journal = {arXiv preprint arXiv:2508.10942},
      year = {2025},
    }

12. Agentic AI

    EvoEmo: Towards Evolved Emotional Policies for LLM Agents in Multi-Turn Negotiation

    Yunbo Long, Liming Xu, Lukas Beckenbauer, and 2 more authors

    arXiv preprint arXiv:2509.04310, 2025

    Abs Bib HTML

    Recent research on Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) has demonstrated that agents can engage in \textitcomplex, \textitmulti-turn negotiations, opening new avenues for agentic AI. However, existing LLM agents largely overlook the functional role of emotions in such negotiations, instead generating passive, preference-driven emotional responses that make them vulnerable to manipulation and strategic exploitation by adversarial counterparts. To address this gap, we present EvoEmo, an evolutionary reinforcement learning framework that optimizes dynamic emotional expression in negotiations. EvoEmo models emotional state transitions as a Markov Decision Process and employs population-based genetic optimization to evolve high-reward emotion policies across diverse negotiation scenarios. We further propose an evaluation framework with two baselines – vanilla strategies and fixed-emotion strategies – for benchmarking emotion-aware negotiation. Extensive experiments and ablation studies show that EvoEmo consistently outperforms both baselines, achieving higher success rates, higher efficiency, and increased buyer savings. This findings highlight the importance of adaptive emotional expression in enabling more effective LLM agents for multi-turn negotiation.

    @article{long2025evoemo,
      title = {EvoEmo: Towards Evolved Emotional Policies for LLM Agents in Multi-Turn Negotiation},
      author = {Long, Yunbo and Xu, Liming and Beckenbauer, Lukas and Liu, Yuhan and Brintrup, Alexandra},
      journal = {arXiv preprint arXiv:2509.04310},
      year = {2025},
    }

13. Agentic AI

    EQ-Knight: A Memory-Augmented LLM Agent for Strategic Affective Gaming in Debt Recovery

    Yunbo Long, Yuhan Liu, Liming Xu, and 1 more author

    arXiv preprint arXiv:2503.21080, 2025

    Abs Bib HTML

    Large language model-based chatbots have enhanced engagement in financial negotiations, but their overreliance on passive empathy introduces critical risks in credit collection. While empathy-driven approaches preserve client satisfaction in benign cases, they fail catastrophically against dishonest debtors–individuals who exploit conciliatory tactics to manipulate terms or evade repayment. Blindly prioritizing "customer experience" in such scenarios leads to creditor vulnerabilities: revenue leakage, moral hazard, and systemic exploitation. To address this, we propose EQ-Knight, an LLM agent that dynamically optimizes emotional strategy to defend creditor interests. Unlike naive empathy-centric bots, EQ-Knight integrates emotion memory and game-theoretic reasoning, powered by a Hidden Markov Model (HMM) to track and predict debtor emotional states. By analyzing both real-time and historical emotional cues, EQ-Knight strategically counters negative emotions (e.g., aggression, feigned distress) while preserving productive debtor relationships. Experiments demonstrate EQ-Knight’s superiority over conventional LLM negotiators: it achieves a 32% reduction in concession losses without compromising recovery rates, particularly in adversarial cases where debtors weaponize negative emotions (e.g., intimidation, guilt-tripping) to coerce concessions. For credit agencies, EQ-Knight transforms LLMs from high-risk "people-pleasers" into strategic emotion-defenders–balancing emotional intelligence with tactical rigor to enforce accountability and deter exploitation.

    @article{long2025eqknight,
      title = {EQ-Knight: A Memory-Augmented LLM Agent for Strategic Affective Gaming in Debt Recovery},
      author = {Long, Yunbo and Liu, Yuhan and Xu, Liming and Brintrup, Alexandra},
      journal = {arXiv preprint arXiv:2503.21080},
      year = {2025},
    }

14. Benchmark

    SynDelay: A Synthetic Dataset for Delivery Delay Prediction

    Liming Xu, Yunbo Long, and Alexandra Brintrup

    arXiv preprint arXiv:2509.05325, 2025

    Abs Bib Code Website

    Artificial intelligence (AI) is transforming supply chain management, yet progress in predictive tasks – such as delivery delay prediction – remains constrained by the scarcity of high-quality, openly available datasets. Existing datasets are often proprietary, small, or inconsistently maintained, hindering reproducibility and benchmarking. We present SynDelay, a synthetic dataset designed for delivery delay prediction. Generated using an advanced generative model trained on real-world data, SynDelay preserves realistic delivery patterns while ensuring privacy. Although not entirely free of noise or inconsistencies, it provides a challenging and practical testbed for advancing predictive modelling. To support adoption, we provide baseline results and evaluation metrics as initial benchmarks, serving as reference points rather than state-of-the-art claims. SynDelay is publicly available through the Supply Chain Data Hub, an open initiative promoting dataset sharing and benchmarking in supply chain AI. We encourage the community to contribute datasets, models, and evaluation practices to advance research in this area.

    @article{xu2025syndelay,
      title = {SynDelay: A Synthetic Dataset for Delivery Delay Prediction},
      author = {Xu, Liming and Long, Yunbo and Brintrup, Alexandra},
      journal = {arXiv preprint arXiv:2509.05325},
      year = {2025},
      eprint = {https://arxiv.org/abs/2509.05325},
    }

15. Dataset

    AnonNet: An Anonymised Real-World Network for Supply Network Analysis

    Liming Xu, Yunbo Long, and Alexandra Brintrup

    2025
16. Dataset

    SimMD: A Simulated Dataset for Multi-Depot Capacitated Vehicle Routing Problems

    Liming Xu, Yunbo Long, and Alexandra Brintrup

    2025
17. Insight

    Complex Adaptive Supply Networks and Resilience: Global Adaptation and Regionalization

    Thomas Choi, Christopher Tang, Brintrup Alexandra, and 2 more authors

    CAPS Research, 2025

    Bib HTML

    @article{choi2025complex,
      title = {Complex Adaptive Supply Networks and Resilience: Global Adaptation and Regionalization},
      author = {Choi, Thomas and Tang, Christopher and Alexandra, Brintrup and Xu, Liming and Destarac, Jean Paul},
      journal = {CAPS Research},
      year = {2025},
    }

2024

1. LLM & KG

   Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models

   Sara AlMahri, Liming Xu, and Alexandra Brintrup

   arXiv preprint arXiv:2408.07705, 2024

   Bib HTML

   @article{almahri2024enhancing,
     title = {Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models},
     author = {AlMahri, Sara and Xu, Liming and Brintrup, Alexandra},
     journal = {arXiv preprint arXiv:2408.07705},
     year = {2024},
   }

2. MARL

   Large-Scale, Fair Cooperative Logistics Enabled by Transformer-based Neural Networks

   Stephen Mak, Liming Xu, Tim Pearce, and 2 more authors

   IEEE Transactions on Intelligent Transportation Systems, 2024

   [Under review]
3. ASC Concept

   Towards Autonomous Supply Chains: Definition, Characteristics, Conceptual Framework, and Autonomy Lifeevels

   Liming Xu, Stephen Mak, Yaniv Proselkov, and 1 more author

   Journal of Industrial Information Integration, 2024

   Bib HTML

   @article{xu2024towards,
     title = {Towards Autonomous Supply Chains: Definition, Characteristics, Conceptual Framework, and Autonomy Lifeevels},
     author = {Xu, Liming and Mak, Stephen and Proselkov, Yaniv and Brintrup, Alexandra},
     journal = {Journal of Industrial Information Integration},
     volume = {42},
     pages = {100698},
     year = {2024},
     publisher = {Elsevier},
   }

4. ASC & MAS

   On Implementing Autonomous Supply Chains: A Multi-Agent System Approach

   Liming Xu, Stephen Mak, Maria Minaricova, and 1 more author

   Computers in Industry, 2024

   Bib HTML

   @article{xu2024implementing,
     title = {On Implementing Autonomous Supply Chains: A Multi-Agent System Approach},
     author = {Xu, Liming and Mak, Stephen and Minaricova, Maria and Brintrup, Alexandra},
     journal = {Computers in Industry},
     volume = {161},
     pages = {104120},
     year = {2024},
     publisher = {Elsevier},
   }

5. Enabling Viable Supply Networks with Cooperative-Distributed Local-Decentralised Financial Control

   Yaniv Proselkov, Liming Xu, and Alexandra Brintrup

   In Complex Systems and Collective Intelligence (CSCI) workshop, CCS’24, 2024
6. Insight

   Multi-Agent Systems and Foundation Models Enable Autonomous Supply Chains: Opportunities and Challenges

   Liming Xu, Sara Almahri, Stephen Mak, and 1 more author

   In , 2024

   Bib HTML

   @inproceedings{xu2024foundation,
     title = {Multi-Agent Systems and Foundation Models Enable Autonomous Supply Chains: Opportunities and Challenges},
     author = {Xu, Liming and Almahri, Sara and Mak, Stephen and Brintrup, Alexandra},
     journal = {IFAC-PapersOnLine},
     volume = {58},
     number = {19},
     pages = {795--800},
     year = {2024},
     publisher = {Elsevier},
   }

7. Metaverse & CV

   Uncovering the Metaverse within Everyday Environments: a Coarse-to-Fine Approach

   Liming Xu, Dave Towey, Andrew P French, and 1 more author

   In Proceedings of the 48th IEEE International Conference on Computers, Software, and Applications (COMPSAC’24), Jul 2024

   Bib HTML

   @inproceedings{xu2024uncovering,
     title = {Uncovering the Metaverse within Everyday Environments: a Coarse-to-Fine Approach},
     author = {Xu, Liming and Towey, Dave and French, Andrew P and Benford, Steve},
     booktitle = {Proceedings of the 48th IEEE International Conference on Computers, Software, and Applications (COMPSAC'24)},
     month = jul,
     year = {2024},
     address = {Osaka, Japan},
     organization = {IEEE Computer Society},
   }

2023

1. MARL

   Fair Collaborative Vehicle Routing: A Deep Multi-Agent Reinforcement Learning Approach

   Stephen Mak, Liming Xu, Tim Pearce, and 2 more authors

   Transportation Research Part C: Emerging Technologies, Jul 2023

   Abs Bib HTML

   Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other. This achieves economies of scale, thus reducing cost, greenhouse gas emissions and road congestion. But which carrier should partner with whom, and how much should each carrier be compensated? Traditional game theoretic solution concepts are expensive to calculate as the characteristic function scales exponentially with the number of agents. This would require solving the vehicle routing problem (NP-hard) an exponential number of times. We therefore propose to model this problem as a coalitional bargaining game solved using deep multi-agent reinforcement learning, where - crucially - agents are not given access to the characteristic function. Instead, we implicitly reason about the characteristic function; thus, when deployed in production, we only need to evaluate the expensive post-collaboration vehicle routing problem once. Our contribution is that we are the first to consider both the route allocation problem and gain sharing problem simultaneously - without access to the expensive characteristic function. Through decentralised machine learning, our agents bargain with each other and agree to outcomes that correlate well with the Shapley value - a fair profit allocation mechanism. Importantly, we are able to achieve a reduction in run-time of 88%.

   @article{mak2023fair,
     title = {Fair Collaborative Vehicle Routing: A Deep Multi-Agent Reinforcement Learning Approach},
     author = {Mak, Stephen and Xu, Liming and Pearce, Tim and Ostroumov, Michael and Brintrup, Alexandra},
     journal = {Transportation Research Part C: Emerging Technologies},
     volume = {157},
     pages = {104376},
     year = {2023},
     publisher = {Elsevier},
   }

2. Agent Modelling

   Financial Ripple Effect in Complex Adaptive Supply Networks: An Agent-based Model

   Yaniv Proselkov, Jie Zhang, Liming Xu, and 4 more authors

   International Journal of Production Research, Jul 2023

   Bib HTML

   @article{proselkov2023financial,
     title = {Financial Ripple Effect in Complex Adaptive Supply Networks: An Agent-based Model},
     author = {Proselkov, Yaniv and Zhang, Jie and Xu, Liming and Hofmann, Erik and Choi, Thomas Y and Rogers, Dale and Brintrup, Alexandra},
     journal = {International Journal of Production Research},
     pages = {1--23},
     year = {2023},
     publisher = {Taylor \& Francis},
   }

3. Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale?

   Stephen Mak, Tim Pearce, Matthew Macfarlane, and 3 more authors

   In NeurIPS 2023 Computational Sustainability: Promises and Pitfalls from Theory to Deployment, Jul 2023

   HTML
4. MARL

   Unlocking Carbon Reduction Potential with Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem

   Stefan Schoepf, Stephen Mak, Julian Senoner, and 3 more authors

   arXiv preprint arXiv:2307.12136, Jul 2023

   Bib HTML

   @article{schoepf2023unlocking,
     title = {Unlocking Carbon Reduction Potential with Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem},
     author = {Schoepf, Stefan and Mak, Stephen and Senoner, Julian and Xu, Liming and Torbj{\"o}rn, Netland and Brintrup, Alexandra},
     journal = {arXiv preprint arXiv:2307.12136},
     year = {2023},
   }

5. Implementation of Autonomous Supply Chains for Digital Twinnings: a Multi-agent Approach

   Liming Xu, Yaniv Proselkov, Stefan Schoepf, and 3 more authors

   In IFAC-PapersOnline, Jul 2023

2022

1. Metaverse

   Connecting Everyday Objects with the Metaverse: A Unified Recognition Framework

   Liming Xu, Dave Towey, Andrew P French, and 1 more author

   In 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC’22), Jun 2022

   Abs DOI Bib HTML

   The recent Facebook rebranding to Meta has drawn renewed attention to the metaverse. Technology giants, amongst others, are increasingly embracing the vision and opportunities of a hybrid social experience that mixes physical and virtual interactions. As the metaverse gains in traction, it is expected that everyday objects may soon connect more closely with virtual elements. However, discovering this “hidden” virtual world will be a crucial first step to interacting with it in this new augmented world. In this paper, we address the problem of connecting phys-ical objects with their virtual counterparts, especially through connections built upon visual markers. We propose a unified recognition framework that guides approaches to the metaverse access points. We illustrate the use of the framework through experimental studies under different conditions, in which an interactive and visually attractive decoration pattern, an Artcode, is used as the approach to enable the connection. This paper will be of interest to, amongst others, researchers working in Interaction Design or Augmented Reality who are seeking techniques or guidelines for augmenting physical objects in an unobtrusive, complementary manner.

   @inproceedings{xu2022connecting,
     title = {Connecting Everyday Objects with the Metaverse: A Unified Recognition Framework},
     author = {Xu, Liming and Towey, Dave and French, Andrew P and Benford, Steve},
     booktitle = {2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC'22)},
     pages = {401--406},
     month = jun,
     year = {2022},
     address = {Online, originally planned in Torino, Italy},
     organization = {IEEE},
     doi = {10.1109/COMPSAC54236.2022.00063},
   }

2021

1. MARL

   Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routing

   Stephen Mak, Liming Xu, Tim Pearce, and 2 more authors

   In NeuralPS’21, Cooperative AI Workshop, Jun 2021

   Bib HTML

   @inproceedings{mak2022coalitional,
     title = {Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routing},
     author = {Mak, Stephen and Xu, Liming and Pearce, Tim and Ostroumov, Michael and Brintrup, Alexandra},
     year = {2021},
     booktitle = {NeuralPS'21, Cooperative AI Workshop},
   }

2. Soft Sys

   Using Metamorphic Relations to Verify and Enhance Artcode Classification

   Liming Xu, Dave Towey, Andrew P French, and 3 more authors

   Journal of Systems and Software, Jun 2021

   Abs Bib HTML

   Software testing is often hindered where it is impossible or impractical to determine the correctness of the behaviour or output of the software under test (SUT), a situation known as the oracle problem. An example of an area facing the oracle problem is automatic image classification, using machine learning to classify an input image as one of a set of predefined classes. An approach to software testing that alleviates the oracle problem is metamorphic testing (MT). While traditional software testing examines the correctness of individual test cases, MT instead examines the relations amongst multiple executions of test cases and their outputs. These relations are called metamorphic relations (MRs): if an MR is found to be violated, then a fault must exist in the SUT. This paper examines the problem of classifying images containing visually hidden markers called Artcodes, and applies MT to verify and enhance the trained classifiers. This paper further examines two MRs, Separation and Occlusion, and reports on their capability in verifying the image classification using one-way analysis of variance (ANOVA) in conjunction with three other statistical analysis methods: t-test (for unequal variances), Kruskal-Wallis test, and Dunnett’s test. In addition to our previously-studied classifier, that used Random Forests, we introduce a new classifier that uses a support vector machine, and present its MR-augmented version. Experimental evaluations across a number of performance metrics show that the augmented classifiers can achieve better performance than non-augmented classifiers. This paper also analyses how the enhanced performance is obtained.

   @article{xu2021using,
     title = {Using Metamorphic Relations to Verify and Enhance Artcode Classification},
     author = {Xu, Liming and Towey, Dave and French, Andrew P and Benford, Steve and Zhou, Zhi Quan and Chen, Tsong Yueh},
     journal = {Journal of Systems and Software},
     volume = {182},
     pages = {111060},
     year = {2021},
     publisher = {Elsevier},
     select = {true},
   }

3. MAS & SC

   Will Bots Take Over the Supply Chain? Revisiting Agent-based Supply Chain Automation

   Liming Xu, Stephen Mak, and Alexandra Brintrup

   International Journal of Production Economics, Jun 2021

   Abs Bib HTML

   Agent-based systems have the capability to fuse information from many distributed sources and create better plans faster. This feature makes agent-based systems naturally suitable to address the challenges in Supply Chain Management (SCM). Although agent-based supply chains systems have been proposed since early 2000; industrial uptake of them has been lagging. The reasons quoted include the immaturity of the technology, a lack of interoperability with supply chain information systems, and a lack of trust in Artificial Intelligence (AI). In this paper, we revisit the agent-based supply chain and review the state of the art. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains; are filling in gaps, leaving the concept applicable to a wider range of functions. For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation. Digital ledgers help securely transfer data between third parties, making agent-based information sharing possible, without the need to integrate Enterprise Resource Planning (ERP) systems. Learning functionality in agents enables agents to move beyond automation and towards autonomy. We note this convergence effect through conceptualising an agent-based supply chain framework, reviewing its components, and highlighting research challenges that need to be addressed in moving forward.

   @article{xu2021will,
     title = {Will Bots Take Over the Supply Chain? {Revisiting} Agent-based Supply Chain Automation},
     author = {Xu, Liming and Mak, Stephen and Brintrup, Alexandra},
     journal = {International Journal of Production Economics},
     volume = {241},
     pages = {108279},
     year = {2021},
     publisher = {Elsevier},
   }

2019

1. PhD Thesis

   Artcode Detection in Images

   Liming Xu

   University of Nottingham, UK, Jun 2019

   Bib HTML

   @phdthesis{xu2020artcode,
     title = {Artcode Detection in Images},
     author = {Xu, Liming},
     year = {2019},
     school = {University of Nottingham, UK},
   }

2018

1. SWE & ML

   Enhancing Supervised Classifications with Metamorphic Relations

   Liming Xu, Dave Towey, Andrew P French, and 3 more authors

   In 2018 IEEE/ACM 3rd International Workshop on Metamorphic Testing (MET’18), May 2018

   Bib HTML

   @inproceedings{xu2018enhancing,
     title = {Enhancing Supervised Classifications with Metamorphic Relations},
     author = {Xu, Liming and Towey, Dave and French, Andrew P and Benford, Steve and Zhou, Zhi Quan and Chen, Tsong Yueh},
     booktitle = {2018 IEEE/ACM 3rd International Workshop on Metamorphic Testing (MET'18)},
     pages = {46--53},
     year = {2018},
     month = may,
     address = {Gothenburg, Sweden},
     organization = {IEEE},
   }

2017

1. CV & AR

   Recognizing the Presence of Hidden Visual Markers in Digital Images

   Liming Xu, Andrew P. French, Dave Towey, and 1 more author

   In Proceedings of the on Thematic Workshops of ACM Multimedia (MM’17), Oct 2017

   Bib HTML

   @inproceedings{xu2017recognizing,
     title = {Recognizing the Presence of Hidden Visual Markers in Digital Images},
     author = {Xu, Liming and French, Andrew P. and Towey, Dave and Benford, Steve},
     booktitle = {Proceedings of the on Thematic Workshops of ACM Multimedia (MM'17)},
     pages = {210--2018},
     year = {2017},
     month = oct,
     address = {Mountain View, CA, USA},
   }

2016

1. HCI & AR

   Accountable Artefacts: the Case of the Carolan Guitar

   Steve Benford, Adrian Hazzard, Alan Chamberlain, and 5 more authors

   In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI’16), May 2016

   Honorable Mentions Award

   Bib HTML

   @inproceedings{benford2016accountable,
     title = {Accountable Artefacts: the Case of the Carolan Guitar},
     author = {Benford, Steve and Hazzard, Adrian and Chamberlain, Alan and Glover, Kevin and Greenhalgh, Chris and Xu, Liming and Hoare, Michaela and Darzentas, Dimitrios},
     booktitle = {Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI'16)},
     pages = {1163--1175},
     address = {San Jose, USA},
     month = may,
     year = {2016},
     note = {Honorable Mentions Award},
   }

2. HCI & Storytelling

   The Carolan Guitar: a Thing that Tells its Own Life Story

   Steve Benford, Adrian Hazzard, and Liming Xu

   Interactions, May 2016

   Bib HTML

   @article{benford2015carolan,
     title = {The Carolan Guitar: a Thing that Tells its Own Life Story},
     author = {Benford, Steve and Hazzard, Adrian and Xu, Liming},
     journal = {Interactions},
     volume = {22},
     number = {3},
     pages = {64--66},
     address = {San Jose, USA},
     month = may,
     year = {2016},
   }

3. HCI & Storytelling

   Experiencing the Carolan Guitar

   Steve Benford, Adrian Hazzard, Alan Chamberlain, and 5 more authors

   In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, May 2016

   Bib

   @inproceedings{benford2016experiencing,
     title = {Experiencing the Carolan Guitar},
     author = {Benford, Steve and Hazzard, Adrian and Chamberlain, Alan and Glover, Kevin and Greenhalgh, Chris and Xu, Liming and Hoare, Michaela and Darzentas, Dimitrios},
     booktitle = {Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems},
     pages = {3651--3654},
     address = {San Jose, USA},
     month = may,
     year = {2016},
   }

2015

1. HCI & AR

   Augmenting a Auitar with its Digital Footprint

   Steve Benford, Adrian Hazzard, Alan Chamberlain, and 1 more author

   In Proceeding of International Conference on New Interfaces for Musical Expressions (NIME’15), May 2015

2014

1. HQ-Tree: A Distributed Spatial Index based on Hadoop

   Jun Feng, Zhixian Tang, Mian Wei, and 1 more author

   China Communications, May 2014

2013

1. A New Spatio-Temporal Prediction Approach based on Aggregate Queries

   Jun Feng, Zhonghua Zhu, Yaqing Shi, and 1 more author

   International Journal of Knowledge and Web Intelligence, May 2013

2012

1. Database

   HQ-Tree: A Hadoop-based Distributed Spatial Index

   Jun Feng, Zhixian Tang, Mian Wei, and 1 more author

   The 3nd Chinese Conference on Cloud Computing (CCCC’12), Sep 2012

   Best Paper Award

   Bib

   @article{feng2012hq,
     title = {HQ-Tree: A Hadoop-based Distributed Spatial Index},
     author = {Feng, Jun and Tang, Zhixian and Wei, Mian and Xu, Liming},
     journal = {The 3nd Chinese Conference on Cloud Computing (CCCC'12)},
     year = {2012},
     month = sep,
     address = {Lishui, Zhejiang, China},
     note = {Best Paper Award},
   }

2. XML & Search

   Node Depth-based Methods for XML Indexing and Searching

   Liming Xu

   Hohai University, China, Sep 2012