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Advances in computational logistics and supply Chain analytics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in computational logistics and supply Chain analytics/ edited by Ibraheem Alharbi ... [et al.].
其他作者:	Alharbi, Ibraheem.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xiv, 196 p. :ill. (chiefly col.), digital ;24 cm.
內容註:	Introduction -- Innovative logistic techniques and Tools in Industry 4.0 -- Predictive analytics for quality control of logistic processes -- Artificial intelligence systems for smart logistics in the medicine sector -- Supply-Chain Management: Automating the purchase process based on machine learning techniques -- Multi Criteria Decision Making applications in Supply Chain Management -- Consumers' Acceptance of Drones for last-mile delivery in emerging economy -- Evaluation methods to mitigate the risks of price fluctuations on inventory in contemporary economies -- Overview of using augmented reality techniques in logistics -- Computational methods for scheduling problems -- Digitalization and automation of supply chain processes -- Reverse Logistics: Evaluation of practices in hospitals in Saudi Arabia -- Combining Global Optimization Algorithms with Live Google Maps for Solving the Vehicle Routing Problem -- Conclusion.
Contained By:	Springer Nature eBook
標題:	Business logistics - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-031-50036-7
ISBN:	9783031500367

Advances in computational logistics and supply Chain analytics
Advances in computational logistics and supply Chain analytics[electronic resource] /edited by Ibraheem Alharbi ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xiv, 196 p. :ill. (chiefly col.), digital ;24 cm. - Unsupervised and semi-supervised learning,2522-8498. - Unsupervised and semi-supervised learning..

Introduction -- Innovative logistic techniques and Tools in Industry 4.0 -- Predictive analytics for quality control of logistic processes -- Artificial intelligence systems for smart logistics in the medicine sector -- Supply-Chain Management: Automating the purchase process based on machine learning techniques -- Multi Criteria Decision Making applications in Supply Chain Management -- Consumers' Acceptance of Drones for last-mile delivery in emerging economy -- Evaluation methods to mitigate the risks of price fluctuations on inventory in contemporary economies -- Overview of using augmented reality techniques in logistics -- Computational methods for scheduling problems -- Digitalization and automation of supply chain processes -- Reverse Logistics: Evaluation of practices in hospitals in Saudi Arabia -- Combining Global Optimization Algorithms with Live Google Maps for Solving the Vehicle Routing Problem -- Conclusion.

This book provides advances in computational logistics and supply chain analytics. The authors include innovative data-driven and learning-based approaches, methods, algorithms, techniques, and tools that have been designed or applied to create and implement a successful logistics and supply chain management process. This book highlights the state of the art and challenges related to the design and the application of computational methods to solve logistic and supply chain management problems. The authors present recent computational logistic methods and supply chain analytics techniques designed and applied to support managers in improving such complex processes. This book broadly covers recent computational methods and techniques applied to ensure continuous improvement of transport, logistic, and supply chain management processes. Readers can rapidly explore these new methods and their applications to solve such complex problems. Highlights the importance of embedding and using computational methods to improve supply chain processes; Presents machine learning and data analytics techniques to solve supply chain optimization problems; Gives readers design and applications of computational methods automate transport, logistic and supply chain processes.

ISBN: 9783031500367

Standard No.: 10.1007/978-3-031-50036-7doiSubjects--Topical Terms:

731545
Business logistics
--Data processing.

LC Class. No.: HD38.5

Dewey Class. No.: 658.7

Advances in computational logistics and supply Chain analytics
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505 0 $a Introduction -- Innovative logistic techniques and Tools in Industry 4.0 -- Predictive analytics for quality control of logistic processes -- Artificial intelligence systems for smart logistics in the medicine sector -- Supply-Chain Management: Automating the purchase process based on machine learning techniques -- Multi Criteria Decision Making applications in Supply Chain Management -- Consumers' Acceptance of Drones for last-mile delivery in emerging economy -- Evaluation methods to mitigate the risks of price fluctuations on inventory in contemporary economies -- Overview of using augmented reality techniques in logistics -- Computational methods for scheduling problems -- Digitalization and automation of supply chain processes -- Reverse Logistics: Evaluation of practices in hospitals in Saudi Arabia -- Combining Global Optimization Algorithms with Live Google Maps for Solving the Vehicle Routing Problem -- Conclusion.
520 $a This book provides advances in computational logistics and supply chain analytics. The authors include innovative data-driven and learning-based approaches, methods, algorithms, techniques, and tools that have been designed or applied to create and implement a successful logistics and supply chain management process. This book highlights the state of the art and challenges related to the design and the application of computational methods to solve logistic and supply chain management problems. The authors present recent computational logistic methods and supply chain analytics techniques designed and applied to support managers in improving such complex processes. This book broadly covers recent computational methods and techniques applied to ensure continuous improvement of transport, logistic, and supply chain management processes. Readers can rapidly explore these new methods and their applications to solve such complex problems. Highlights the importance of embedding and using computational methods to improve supply chain processes; Presents machine learning and data analytics techniques to solve supply chain optimization problems; Gives readers design and applications of computational methods automate transport, logistic and supply chain processes.
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