Dynamics and Resilience in Sustainable Industrial Design: A Multi-Scale Review

Abstract

Engineers are increasingly confronted with the challenge of designing sustainable processes, industrial networks, and supply chains in an era of unprecedented complexity and uncertainty. Conventional steady- state sustainability assessment methodologies, while valuable, are fundamentally inadequate for addressing the dynamic complexities inherent in multiscale industrial systems. This paper presents a comprehensive review of emerging approaches that integrate nonlinear dynamics and resilience thinking into sustainable industrial design. The review systematically examines the evolution of sustainability assessment frameworks, highlighting critical limitations of static approaches in capturing nonlinearity, feedback loops, and temporal variability across multiple scales. We explore the transition to dynamic, data- driven methodologies, emphasizing hybrid mechanistic- machine learning models that bridge first- principles knowledge with empirical data. Further, we examine transdisciplinary concepts adapted from ecological sciences—including stability landscapes and adaptive cycles—demonstrating how resilience thinking fundamentally redefines process design and control strategies in the face of uncertainties. The emergence of advanced computational tools, including spatio- temporal deep learning architectures and adaptive process control strategies, is reshaping the field of sustainable industrial network design. By synthesizing resilience principles with dynamic sustainability assessments, this review establishes a transformative paradigm for industrial engineering research and practice.