國立高雄大學圖資館 |

Language: English

Back

Advances in computational logistics and supply Chain analytics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advances in computational logistics and supply Chain analyticsedited by Ibraheem Alharbi ... [et al.].
other author:	Alharbi, Ibraheem.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xiv, 196 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Business logisticsData processing.
Online resource:	https://doi.org/10.1007/978-3-031-50036-7
ISBN:	9783031500367$q(electronic bk.)

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$q(electronic bk.)

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

230874
Business logistics
--Data processing.

LC Class. No.: HD38.5

Dewey Class. No.: 658.7

Advances in computational logistics and supply Chain analytics
LDR:03280nmm a22003375a 4500 001 664913
003 DE-He213
005 20240320135237.0
006 m d
007 cr nn 008maaau
008 241219s2024 sz s 0 eng d
020 $a 9783031500367$q(electronic bk.)
020 $a 9783031500350$q(paper)
024 7 $a 10.1007/978-3-031-50036-7 $2 doi
035 $a 978-3-031-50036-7
040 $a GP $c GP
041 0 $a eng
050 4 $a HD38.5
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 658.7 $2 23
090 $a HD38.5 $b .A244 2024
245 0 0 $a Advances in computational logistics and supply Chain analytics $h [electronic resource] / $c edited by Ibraheem Alharbi ... [et al.].
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xiv, 196 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Unsupervised and semi-supervised learning, $x 2522-8498
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.
650 0 $a Business logistics $x Data processing. $3 230874
650 0 $a Business logistics $x Automation. $3 345935
650 0 $a Business logistics $x Decision making. $3 455610
650 0 $a Telecommunication. $3 182422
650 0 $a Business logistics. $3 199351
650 0 $a Facility management. $3 191521
650 0 $a Industrial management. $3 174961
650 1 4 $a Communications Engineering, Networks. $3 273745
650 2 4 $a Supply Chain Management. $3 737140
650 2 4 $a Facility Management. $3 273742
650 2 4 $a Industrial Management. $3 910688
700 1 $a Alharbi, Ibraheem. $3 977666
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Unsupervised and semi-supervised learning. $3 834422
856 4 0 $u https://doi.org/10.1007/978-3-031-50036-7
950 $a Engineering (SpringerNature-11647)