04290nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001800153072001600171072002300187072001500210245018000225250001800405264006900423300006500492336002600557337002600583338003600609347002400645490005800669505094600727520128001673650002302953650002402976650002503000650002703025650004203052650002903094650002503123650002703148700008003175700008603255700007703341700008203418710003403500773002603534776003603560776003603596776003603632830005803668856004603726978-3-031-50036-7DE-He21320251030111732.0cr nn 008mamaa240320s2024    sz |    s    |||| 0|eng d  a97830315003677 a10.1007/978-3-031-50036-72doi 4aTK5101-5105.9 7aTJK2bicssc 7aTEC0410002bisacsh 7aTJK2thema10aAdvances in Computational Logistics and Supply Chain Analyticsh[electronic resource] /cedited by Ibraheem Alharbi, Chiheb-Eddine Ben Ncir, Bader Alyoubi, Hajer Ben-Romdhane.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIV, 196 p. 40 illus., 30 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aUnsupervised and Semi-Supervised Learning,x2522-84980 aIntroduction -- 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. .  aThis 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. . 0aTelecommunication. 0aBusiness logistics. 0aFacility management. 0aIndustrial Management.14aCommunications Engineering, Networks.24aSupply Chain Management.24aFacility Management.24aIndustrial Management.1 aAlharbi, Ibraheem.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aBen Ncir, Chiheb-Eddine.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aAlyoubi, Bader.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aBen-Romdhane, Hajer.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303150035008iPrinted edition:z978303150037408iPrinted edition:z9783031500381 0aUnsupervised and Semi-Supervised Learning,x2522-849840uhttps://doi.org/10.1007/978-3-031-50036-7