Data science and big data analytics (DS &BDA) methodologies and tools are used
extensively in supply chains and logistics (SC &L). However, the existing insights are …

The ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …

Industry 5.0 is a combination of organisational principles and technologies to design and
manage operations and supply chains as resilient, sustainable, and human-centric systems …

A large variety of models have been developed in the last two decades aiming at supply
chain (SC) stress-testing and resilience. New digital and artificial intelligence (AI) …

Our research strives to examine how simulation models of logistics systems can be
produced automatically from verbal descriptions in natural language and how human …

Supply chain resilience is on the agenda of academia and industry like never before. One
strong instigator for this phenomenon has been the COVID-19 pandemic, which opened the …

With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …

Consumers' dramatic demand has a pernicious effect throughout the supply chain. It
exacerbates inventory distortion because of significant revenue loss caused by stock-level …

Network models are an essential block of modern networks. For example, they are widely
used in network planning and optimization. However, as networks increase in scale and …

Abstract Artificial Neural Networks (ANNs) are a type of machine learning algorithm inspired
by the structure and function of the human brain. In the context of supply chain management …