Value-Centered Approaches to Intelligent Logistics Coordination: Bridging Effectiveness and Social Justice
Abstract
The increasing integration of artificial intelligence (AI) into logistics systems has transformed supply chain coordination, enabling high-precision decision-making, real-time optimization, and predictive analytics. However, this technological advancement has also introduced ethical concerns regarding fairness, transparency, and equitable resource distribution. This paper investigates value-centered approaches to intelligent logistics coordination, emphasizing the dual objective of operational effectiveness and social justice.
Drawing upon foundational works in pattern recognition, image analysis, and AI-driven decision systems (Laurentini, 1994; Berg et al., 2005; Zhang & Liu, 2005), this research conceptualizes logistics systems as adaptive intelligence networks capable of dynamic optimization. Techniques such as segmentation, detection, and structural correspondence are reinterpreted as metaphors for supply chain decomposition, node matching, and route optimization. Classical methodologies such as the Hough transform (Hough, 1959) and region-based segmentation models (Jianping et al., 2001) are leveraged conceptually to describe decision boundaries and resource clustering in logistics environments.
Furthermore, the study integrates ethical AI considerations, particularly focusing on fairness-aware optimization in supply chain systems. The work of Raikar et al. (2026) is central to this discussion, highlighting the tension between efficiency maximization and equitable distribution of resources in AI-driven systems. This ethical framing is used to construct a hybrid logistics coordination model that balances performance metrics with normative constraints.
The research adopts a conceptual synthesis methodology, integrating algorithmic principles from computer vision literature with socio-technical systems theory. Findings suggest that logistics coordination systems can achieve both high efficiency and ethical alignment when fairness constraints are embedded into optimization layers. However, trade-offs persist in scalability, computational complexity, and real-time adaptability.
The paper contributes a novel interdisciplinary framework connecting AI-based perception models with logistics ethics, offering implications for sustainable supply chain governance and intelligent system design.
Keywords
Intelligent logistics, AI coordination, supply chain ethics, fairness optimization
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