Computational Intelligence Techniques for Sustainable Supply Chain Management presents state-of-the-art computational intelligence techniques and applications for supply chain sustainability issues and logistic problems, filling the gap between general textbooks on sustainable supply chain management and more specialized literature dealing with methods for computational intelligence techniques. This book focuses on addressing problems in advanced topics in the sustainable supply chain and will appeal to practitioners, managers, researchers, students, and professionals interested in sustainable logistics, procurement, manufacturing, inventory and production management, scheduling, transportation, and supply chain network design.

Against this background, and in order to facilitate a broad discussion on the contribution of IT toward implementing the UN SDGs, this book is being produced. The book gathers inputs from universities, enterprises, and research organizations working on matters related to IT and sustainable development in a variety of contexts. It also provides a platform for the dissemination of information on the latest initiatives, paving the way for technology transfer and networking. Last but not least, a further aim of the book is to present methodological approaches and experiences deriving from case studies and projects, which aims to show how IT may support sustainability efforts in a wide range of settings and contexts. Digital technologies are now widely spread and the variety of tools and methods available today means that they can potentially useful in helping to achieve the United Nations Sustainable Development Goals (UN SDGs). However, the gap between technological innovation and sustainable development applications is quite wide. For instance, it is unclear how artificial intelligence can be leveraged to tackle some of the world’s biggest environmental challenges, including climate change, biodiversity, and water management. Also, there is a need to highlight how innovative green technologies and their applications in areas such as renewable energy, waste management, and sustainable agriculture may be used and to showcase how technological innovation can lead to more sustainable production and consumption patterns.

In this book, a risk management approach starts off by discussing important issues related to managing supply chain disruption risks from various perspectives during VUCA times. It explores the essence and principles relating to managing these risks and provides the framework and multi-goal model groups for managing such unknown-unknown risks and subsequent disruptions at a global scale.

The book explores and presents the latest developments across different emerging topics in supply chain risk and disruption management. These include (i) an overview of supply chain risk, and disruption management tools, techniques, and approaches, (ii) a review on uncertainty modeling for decentralized supply chain systems, (iii) supply chain deep uncertainties and risks - the ''new normal'', (iv) emergent technologies for supply chain risk and disruption management, (v) supply chain resilience strategies for times of unprecedented uncertainty, (vi) the role of blockchain in developing supply chain resilience against disruptions, (vii) a qualitative study on supply chain risk management adopting blockchain technology, (viii) assessment of risks and risk management for agriculture supply chain, (ix) resilience of agri-food supply chains: Australian developments after a decade of supply and demand shocks, (x) prioritization of risks in the pharmaceutical supply chains (xi) improving medical supply chain disruption management with the blockchain technology, and (xii) impacts of resilience practices on supply chain sustainability.

Computational Intelligence Techniques for Sustainable Supply Chain Management presents state-of-the-art computational intelligence techniques and applications for supply chain sustainability issues and logistic problems, filling the gap between general textbooks on sustainable supply chain management and more specialized literature dealing with methods for computational intelligence techniques. This book focuses on addressing problems in advanced topics in the sustainable supply chain and will appeal to practitioners, managers, researchers, students, and professionals interested in sustainable logistics, procurement, manufacturing, inventory and production management, scheduling, transportation, and supply chain network design.

Against this background, and in order to facilitate a broad discussion on the contribution of IT toward implementing the UN SDGs, this book is being produced. The book gathers inputs from universities, enterprises, and research organizations working on matters related to IT and sustainable development in a variety of contexts. It also provides a platform for the dissemination of information on the latest initiatives, paving the way for technology transfer and networking. Last but not least, a further aim of the book is to present methodological approaches and experiences deriving from case studies and projects, which aims to show how IT may support sustainability efforts in a wide range of settings and contexts. Digital technologies are now widely spread and the variety of tools and methods available today means that they can potentially useful in helping to achieve the United Nations Sustainable Development Goals (UN SDGs). However, the gap between technological innovation and sustainable development applications is quite wide. For instance, it is unclear how artificial intelligence can be leveraged to tackle some of the world’s biggest environmental challenges, including climate change, biodiversity, and water management. Also, there is a need to highlight how innovative green technologies and their applications in areas such as renewable energy, waste management, and sustainable agriculture may be used and to showcase how technological innovation can lead to more sustainable production and consumption patterns.

In this book, a risk management approach starts off by discussing important issues related to managing supply chain disruption risks from various perspectives during VUCA times. It explores the essence and principles relating to managing these risks and provides the framework and multi-goal model groups for managing such unknown-unknown risks and subsequent disruptions at a global scale.

The book explores and presents the latest developments across different emerging topics in supply chain risk and disruption management. These include (i) an overview of supply chain risk, and disruption management tools, techniques, and approaches, (ii) a review on uncertainty modeling for decentralized supply chain systems, (iii) supply chain deep uncertainties and risks - the ''new normal'', (iv) emergent technologies for supply chain risk and disruption management, (v) supply chain resilience strategies for times of unprecedented uncertainty, (vi) the role of blockchain in developing supply chain resilience against disruptions, (vii) a qualitative study on supply chain risk management adopting blockchain technology, (viii) assessment of risks and risk management for agriculture supply chain, (ix) resilience of agri-food supply chains: Australian developments after a decade of supply and demand shocks, (x) prioritization of risks in the pharmaceutical supply chains (xi) improving medical supply chain disruption management with the blockchain technology, and (xii) impacts of resilience practices on supply chain sustainability.