FROM ANT COLONIES TO INDUSTRIAL METABOLISM

This research introduces a novel theoretical and applied framework bridging bio-inspired Ant Colony Optimization (ACO) algorithms with Industrial Metabolism concepts to address contemporary manufacturing challenges requiring both efficiency and efficacy. Traditional industrial systems optimize for efficiency (doing things right) but struggle with efficacy (doing the right things) in dynamic environments. We reconceptualize industrial systems as metabolic organisms rather than mechanical assemblies, drawing on complexity theory (Kauffman, Mossio, Montévil) to develop the Industrial Metabolism Framework (IMF). The framework integrates: (1) autocatalytic network theory, (2) stigmergic coordination mechanisms, (3) adjacent possible exploration, and (4) emergent synchronization patterns. We demonstrate theoretical connections between biological foraging, algorithmic implementation, and industrial application through three analytical lenses: topological, dynamical, and thermodynamic. Conceptual validation across three case studies (reverse logistics, residential energy management, anti-speculative supply chains) shows how distributed optimization achieves computational efficiency while enabling systemic efficacy—adaptability, resilience, and circular economy alignment. Key contributions include: explicit differentiation between efficiency and efficacy metrics, formalization of autocatalytic closure in industrial systems, extension of Adjacent Possible to innovation spaces, and metrics for "industrial vitality" analogous to biological health indicators. This work provides practitioners with diagnostic methodology, parameter tuning guidelines, and applicability boundaries. For researchers, it opens pathways for empirical validation and hybrid optimization approaches. Keywords: Ant Colony Optimization, Industrial Metabolism, Distributed Systems, Manufacturing Innovation, Circular Economy, Complex Adaptive Systems