The increasing digitalisation of processes in logistics and the need for more integrated and seamless cooperation in logistics, supply chain, inventory, and production management belong to the current dominant trends in the business world. Moreover, the pressure to reduce CO2 emissions and develop more resource-efficient business and waste management models strongly influence the organisation of logistics operations on both a local and global scale. The integration of physical and cyber systems is necessary to achieve more environmentally friendly, efficient, and safe logistics, supply chain, inventory, and production operations.

The continuously expanding pools of managerial data and sets of environmental and technical constraints have triggered the development of new mathematical models. These could potentially be the foundations of more advanced simulation and decision-making techniques, solving an urgent problem for the business and economic areas of logistics, supply chain, inventory, and production management. All of these emerging problems are facing the high obstacle of uncertainty that has become the striking characteristic of the many items included in the aforementioned operations, at every stage and every moment. Here, we also mention stochastic disruption and regime-switching. All the smart processes of learning, improvement, optimisation, and control under uncertainty are the core purposes of modern operations research, data mining, analytics, machine learning, and artificial intelligence (AI) that will be discussed, used and refined in this Special Issue.

This Special Issue aims to collect research from a wide range of subjects in the fields of logistics, supply chain management, and related topics that employ and further create advanced operations research (OR) and management science (MS) approaches, theories, and methods. The potential scope therefore includes but is not limited to, methodologies and challenges of logistics (L), supply chain management (SCM), inventory management (IM) and production management (PM). We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Smart decision methods for sustainable supply chain management
• Waste management
• Green sourcing and procurement
• Green routing with time windows and intermediate depots
• Reverse logistics
• Sustainability assessment
• Machine learning, IoT, and AI applications
• Sustainable and biological energy production
• Resilience strategy
• Two-warehouse probabilistic model with price discount
• Data mining and analytics of stochastic disruption and regime switching
• Operations research methods in solving smart and sustainable supply chain and logistics issues