Deadline Date: 31 October 2026
Achieving sustainable industrial and service operations requires engineering solutions that turn data into actionable decisions and optimize systems in real-world settings. This Special Issue, entitled "Data-Driven Modelling and Intelligent Optimization for Green Engineering and Service Operations," aims to bring together research that demonstrates how computational engineering methods can be applied to solve practical sustainability challenges.
We invite contributions focused on developing and deploying techniques such as machine learning, simulation, predictive analytics, and multi-objective optimization to improve energy use, reduce waste, and enhance the efficiency of manufacturing processes, logistics networks, and service systems.
The issue emphasizes work that bridges the gap between methodology and implementation, highlighting engineering approaches that have been tested, validated, or applied in industrial or service contexts to support greener operations.
The following subtopics are the particular interests of this special issue, including but not limited to:
Machine learning for energy and resource forecasting
Optimization of low-carbon production and logistics systems
Simulation and digital twins for sustainable service operations
Lifecycle quality assessment integrated with operational decision-making
Statistical process control for low‑waste manufacturing
Data‑driven circular economy and waste reduction models
Resilience and risk analysis in sustainable supply networks
Human-AI collaboration for eco-efficient operations
Editorial board
