The current world’s context has challenged supply chains, especially with regard to resilience and sustainability. The COVID-19 pandemic brought unprecedented issues to supply chains in terms of maintaining their continuity in delivering products and services. Concurrently, the world is facing many never seen climate issues, which have fostered discussions on how supply chains can produce and deliver products and services in a more sustainable way. At the same time that pandemic and climate issues have arisen in the world, the fourth industrial revolution (Industry 4.0) brings many opportunities for supply chains by the adoption of disruptive technologies. This includes data, information and knowledge technologies, which are integrated with physical technologies allowing for the generation of more efficient, integrated, transparent and smarter supply chain processes.

The advent of wearable devices, Internet of Things, and Internet of Vehicles have stimulated deep transformations in supply chains, not only at the technological level but also at the societal and economic level. Data is generated at a rate of petabytes per day, and given this amount of data, intelligent processing is needed. In addition, because of the advances in high-performance computing, large data sets can now be used for training machine learning algorithms. Specifically, deep learning paradigms enable the sophisticated transformation of data into usable, operational knowledge. Moreover, discussions on how supply chains can act for more sustainable and smart societies (Society 5.0) are also ongoing. There is a demand to further explore the abundant applications of soft computing methods, including deep learning, fuzzy logic, evolutionary methods, and various data mining techniques.

Therefore, this Special Issue aims to answer the key question of how the application of machine learning and computational intelligence can contribute to more sustainable and resilient supply chains. We welcome both original research and review articles with a focus on the application of machine learning and computational intelligence in supply chains.

Potential topics include but are not limited to the following:

• Big data analytics in supply chains
• Internet of Things in supply chains
• Artificial intelligence and machine learning in supply chains
• Blockchain technology in supply chains
• Cloud technologies in supply chains
• Digital twins in supply chains
• Robotics and autonomous vehicles in supply chains
• Cobots and multi-agent systems in supply chains
• Additive manufacturing in supply chains
• Augmented and virtual reality in supply chains
• Interoperability of technologies in supply chains
• AI-based and green-based supply chains
• Data-driven innovations for planning and management in the supply chains
• Soft computing methods for supply chains
• Meta-heuristic algorithms in supply chain
• Computational intelligence for sustainable supply chains
• Novel or improved nature-inspired optimization algorithms in supply chains
• Generative adversarial learning in supply chains
• Intelligent transportation systems
• Advanced machine learning and deep networks for supply chains
• Trend analysis with big data and artificial intelligence for supply chains