Integrating Machine Learning, Optimization, and Risk-Aware Design for Resilient Low-Carbon Cold Chain Logistics

Abstract

Background: Cold chain logistics constitute a critical backbone for food, pharmaceutical, and perishable goods distribution. Rapid technological change, heightened regulatory scrutiny, and sustainability pressures have created a need for integrative scientific frameworks that blend machine learning, optimization algorithms, risk assessment, and system design to guarantee quality, compliance, and environmental performance in cold chains (Emergentcold, 2023; Maersk, 2023; Chowdhury, 2025). Methods: This research synthesizes thematic findings across empirical and methodological studies in cold chain routing and inventory optimization, machine learning–based quality prediction, distribution channel selection, and risk-assessment methodologies. Using a structured integrative review approach grounded in decision-science and systems engineering principles, the paper constructs a unified conceptual framework that maps data-driven prediction, optimization modules, and FMEA-QFD risk controls into a coherent operational cycle (Andrejic et al., 2023; Pajic et al., 2023; Theeb et al., 2020).

Results: The framework articulates how multi-objective metaheuristics (ant-colony, hybrid genetic algorithms), inventory-allocation formulations, and FUCOM-ADAM weighting for channel selection can be combined with supervised and transfer-learning models for freshness and temperature prediction to drive routing and storage decisions that minimize quality loss and emissions simultaneously (Zhao et al., 2020; Maroof et al., 2024; Qiao et al., 2022; Kim et al., 2022). The framework also shows how real-time sensing and predictive models enable compliance and proactive corrective control in pharmaceutical supply chains (Chowdhury, 2025). Conclusions: Implementing the proposed integrative approach yields a pathway to resilient, low-carbon cold chains that balance quality assurance, regulatory compliance, and environmental objectives. The paper discusses theoretical implications, operational trade-offs, limits of current evidence, and avenues for empirical validation and policy interventions. The contribution is a rigorous, citation-grounded blueprint for researchers, logistics managers, and policymakers seeking to harmonize machine learning, optimization, and risk management in contemporary cold chain systems (Emergentcold, 2023; Tessol, 2023; Maersk, 2023).