Modern supply chains face mounting uncertainty and scale, motivating the
integration of Artificial Intelligence (AI) and Machine Learning (ML) with mathematical
optimization to enable robust and adaptive decisions. We present a
systematic review of 199 articles on tangible supply chains, categorizing how ML
is used—primarily for parameter estimation and for solution generation—and
proposing a taxonomy that links ML roles to problem types and optimization
formulations. The review surfaces consistent patterns (e.g., reinforcement learning
in logistics), identifies underexplored areas (e.g., ML-aided reformulation and
learned uncertainty for robust/DRO), and introduces a research framework to
orient future studies. A dedicated subsection examines how these integrations
relate to supply chain viability and resilience.
Keywords: Supply chain, Optimization, Machine learning, Logistics