Research

Research Logistics & Transportation Networks

AI-Driven Dynamic Route Optimization and Autonomous Delivery Systems: Enhancing Logistics Efficiency

This paper explores the transformative impact of AI in logistics, focusing on dynamic route optimization and autonomous delivery systems. It highlights how AI can enhance efficiency, reduce costs, and improve service delivery, while also addressing the challenges and limitations of these technologies.

2026-09-29

Research

Securing Agentic AI: Addressing Key Vulnerabilities in Autonomous Agents

This paper explores the multifaceted security challenges in agentic AI, focusing on prompt injection, memory poisoning, tool integrity, inter-agent communication, and model routing. The authors highlight the need for a holistic approach to ensure the integrity and provenance of autonomous agents, particularly in critical domains like supply chain management.

2026-09-28

Research Logistics & Transportation Networks

Venture Capital Investments and Syndication Networks: Central Network Positions Expand Distant Investments

This paper by Olav Sorenson and Toby E. Stuart examines how interfirm networks in the U.S. venture capital (VC) market influence the spatial distribution of investments. The study reveals that venture capitalists with central positions in syndication networks are more likely to invest in spatially distant companies, expanding their investment radius.

2026-09-27

Research Digital, Intelligence & Platforms

A Comprehensive Survey on MLOps Tool Support for Efficient ML Lifecycle Management

This paper provides a detailed survey of MLOps tool support, focusing on the integration of Machine Learning (ML) practices with DevOps principles. The study evaluates various tools, including Kubeflow, MLFlow, DataRobot, and others, highlighting their features, limitations, and practical implications for efficient ML lifecycle management.

2026-09-26

Research Digital, Intelligence & Platforms

Knowledge Graphs: Enhancing Supply Chain with Improved Accuracy in QA

This paper, authored by Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and Philip S. Yu, provides a comprehensive review of knowledge graphs (KGs) and their applications. The authors explore the representation, acquisition, and use of KGs, highlighting their potential to enhance decision-making in the supply chain through improved accuracy in question answering (QA) and other AI-driven tasks.

2026-09-25

Research Digital, Intelligence & Platforms

Neural Combinatorial Optimization for Vehicle Routing: A 4-Category Framework

This paper provides a comprehensive survey of Neural Combinatorial Optimization (NCO) algorithms for solving Vehicle Routing Problems (VRPs), categorizing them into four distinct types: Learning to Construct (L2C), Learning to Improve (L2I), Learning to Predict-Once (L2P-O), and Learning to Predict-Multiplicity (L2P-M). The study highlights the current limitations and future directions in NCO, aiming to enhance the efficiency and scalability of VRP solutions.

2026-09-23

Research Digital, Intelligence & Platforms

Transformer Achieves 28.4 BLEU on WMT 2014 English-to-German Translation

The Transformer, a novel neural network architecture introduced by Ashish Vaswani, Noam Shazeer, and colleagues, achieves state-of-the-art results in machine translation tasks. By relying solely on self-attention mechanisms, the Transformer outperforms traditional recurrent and convolutional models, offering superior performance, faster training, and better parallelization.

2026-09-20

Research Digital, Intelligence & Platforms

Enhancing Recommender Systems with Graph Neural Networks: A Comprehensive Survey

Recommender systems (RS) are essential for navigating the vast array of products and services online. Traditional RS, such as content-based and collaborative-filtering, struggle with complex, non-Euclidean data like Knowledge Graphs (KG). This survey by Gao et al. provides a detailed taxonomy of GNN-based Knowledge Aware Deep Recommender (GNN-KADR) systems, highlighting their effectiveness in addressing practical recommendation issues.

2026-09-19

Research Logistics & Transportation Networks

Digital Transformation Drives Malleable Organizational Designs and Ecosystems

This systematic review of 279 articles on digital transformation (DT) reveals that DT leads to malleable organizational designs and is embedded in digital business ecosystems. The study provides a multi-dimensional framework and four perspectives on DT, offering valuable insights for both academics and practitioners.

2026-09-18

Research Logistics & Transportation Networks

Digital Transformation: A Thematic Review and Future Research Opportunities

This paper provides a comprehensive review of digital transformation, synthesizing 58 peer-reviewed studies and integrating insights from technological disruption and corporate entrepreneurship. The study identifies nine core themes within two aggregate dimensions—technology and actor—highlighting the importance of an integrated approach to digital transformation.

2026-09-16

Research

Temporal Knowledge Graphs: A Comprehensive Survey on Representation Learning and Applications

This paper provides a comprehensive survey of temporal knowledge graph (TKG) representation learning and its applications, addressing the dynamic evolution of facts over time. It categorizes TKGRL methods into ten distinct categories and discusses their performance and practical implications in various domains, including supply chain management.

2026-09-13

Research Logistics & Transportation Networks

Network Mode Optimization Tool Increases DHL’s Profits by More Than 15%

Dang, Singh, and Allen developed the Network Mode Optimization Tool (NMOT) to address the Vehicle Routing Problem with Time Regulations and Common Carriers (VRPTRCC) for DHL Supply Chain. The tool, which uses ant-colony optimization (ACO), has significantly reduced transportation costs, improved bidding processes, and increased profits by more than 15%.

2026-09-12