Evolvable Multi-Agent Hierarchical Architecture for Contested Logistics Ecosystems
availability, and cooperative participants. These fail in supply chains contending with volatility, adversarial interference, and regulatory fragmentation.
A key challenge is the mismatch between operational complexity and system design. Modern logistics ecosystems span multiple organisations, jurisdictions, and platforms. Data is often heterogeneous, delayed, or incomplete, and, critically in contested settings, intentionally falsified. A fundamental distinction must be drawn between uncertain or degraded data and deliberately deceptive data: bad data and malicious data require different architectural responses. [ 13 ] Many current approaches conflate these, treating adversarial manipulation as a data quality problem.
Advances in AI and analytics enable improved prediction and automation, but current approaches often focus on isolated solutions within bounded domains, assuming consistent data quality and centralised control. [ 6 ][ 8 ] In contested environments, these assumptions produce brittle solutions. Interoperability, human-AI alignment, and accountability under degraded conditions, and adversarial resilience are frequently treated as implementation concerns rather than architectural requirements.[ 3 ][ 12 ]
The research gap addressed is the lack of architectural patterns enabling AI-enabled logistics systems to function as resilient, evolvable ecosystems. A reference architecture is required to( i) supports distributed autonomy while preserving coordinated intent;( ii) treats data trust, provenance, and adversarial validity as first-class concerns;( iii) integrates human judgement alongside autonomous decision-making; and( iv) remains adaptable as technologies, threats, and business priorities evolve.
4 ARCHITECTURAL PRINCIPLES FOR RESILIENT AND ADAPTIVE LOGISTICS ECOSYSTEMS
Resilience, adaptability, and interoperability require architectures explicitly designed to operate under uncertainty, evolve over time, and coordinate decision-making across heterogeneous actors. This section articulates five principles guiding the proposed architecture.
4.1 EVOLVABILITY AS A FIRST-CLASS DESIGN OBJECTIVE
Logistics ecosystems face continuous change from shifting demands, regulatory environments, emerging technologies, and evolving threat landscapes. Architectures optimised for fixed assumptions degrade as conditions change. Drawing on the concept of evolvable manufacturing systems, [ 4 ] evolvability is treated as a first-class architectural objective: the capacity to adapt incrementally while maintaining operational continuity. The architecture supports incremental introduction, modification, and retirement of capabilities through modular decomposition, clear interfaces, and separation of concerns across decision layers.
4.2 HIERARCHICAL AUTONOMY AND SEPARATION OF DECISION CONCERNS
Logistics decisions span multiple levels of abstraction and timescales. Managing this through a single, centralised mechanism produces brittle systems. [ 5 ] The architecture adopts hierarchical
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