Innovations in Computational Logistics and Supply Chain Analytics
Description
This book discusses advances in computational logistics and supply chain analytics. The book includes 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. The book describes new applications of machine learning and data analytic techniques to solve transport, logistic, and supply chain optimization problems. The book gives readers an overview of innovative design and applications of computational methods issued from machine learning and data analytics domains to automate and improve transport, logistic and supply chain processes. The authors also highlight the importance of embedding and using computational methods to improve transport, logistic and supply chain processes. The authors introduce case studies of logistic and supply chain processes improved using innovative learning-based or data-driven methods.
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Pr. Ibraheem Mubarak Alharbi received a BA degree from King Abdul Aziz University, Saudi Arabia, in 2002, a Master's and PhD degree from La Trobe University, Australia, in 2009. Currently, he serves as Full Professor in Department of Management Information Systems, College of Business, University of Jeddah, Jeddah, Saudi Arabia. His research interests include business and information ethics, information privacy and electronic commerce. He has published many research articles in reputed journals and participated in many international conferences.
Dr. Chiheb-Eddine Ben Ncir currently is Associate Professor at the University of Jeddah, Saudi Arabia and member of LARODEC laboratory (University of Tunis). He received his Ph.D in Computer science & management from Higher Institute of Management, University of Tunis, in 2014 and a HDR degree (Habilitation for the Supervision of Doctoral Research) in 2021. He occupied the position of Assistant professor at the Higher School of Digital Economy (University of Manouba, Tunisia) from 2015 to 2018. He is also a business intelligence and big data instructor at the IBM North Africa and Middle East. His research interests concern machine learning methods and data mining tools with a special emphasis on Big data clustering, disjoint and non-disjoint partitioning, kernel methods, as well as many other related fields. He is the author or co-author of more than 50 publications in several prestigious journals and conferences. He is a regular reviewer for many refereed international journals and is a co-editor of some Springer books.
Dr. Alaa A. Qaffas, Associate Professor, is currently the Vice-Dean for Research and Innovation, College of Business, University of Jeddah. Dr. Qaffas first received his B. Sc. degree in Computer Science from King Abdul-Aziz University, Jeddah, Saudi Arabia, in 2007. This was followed in 2010 by his award of the M. Sc. degree in computer Science from Hertfordshire University, Hatfield, United Kingdom. In 2016, Dr. Alaa has awarded the Ph.D. degree in Computer Science from the University of Warwick, Coventry, United Kingdom. He has over 17 years of experience in both academic and administration work. His research interests include cloud computing, cybersecurity, deep learning, intelligent systems, and business data analytics.
Dr Abdullah Alabdulkarim was appointed as Executive Director of the SLA by The Board of Directors of the Saudi Logistics Academy (SLA). Dr Abdullah is tasked with accomplishing the strategic objective of the SLA to create a world-class logistics and supply chain training center in the Kingdom of Saudi Arabia. Dr Abdullah gained his PhD in Industrial Engineering from Cranfield University. His research interest is in Simulation to be applied in different application such as maintenance, Healthcare etc. He has a wide range of practical expertise in strategic planning and implementation, business process, research, and innovation. He previously worked in a range of sectors from Education, and aviation, to financial regulatory institutions. He also, sits in the board of some private and non-profit organizations.
Dr. Monia Rekik is currently a full professor at the Department of operations and decision systems at Université Laval, Canada. She is member of the interuniversity research center on enterprise networks, logistics and transport (CIRRELT), of Centre de recherche CHU de Québec- Université Laval, and the cardiometabolic health, diabetes and obesity research network (CMDO). She holds an engineering degree in industrial engineering from the National School of Engineers of Tunis, Tunisia (ENIT), an M.A.SC in applied mathematics and a PhD in mathematical engineering from Polytechnique Montréal, Canada. She has expertise in mathematical modeling, combinatorial optimization and operations research techniques applied to different fields in industrial and healthcare sectors. Her main works are related to the optimization of personnel scheduling, production planning and scheduling, supply chain management, combinatorial auctions for transport operations procurement, and type 1 diabetes management. She has carried out numerous projects in collaboration with public and private organizations.
Content
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 .- MultiCriteria Decision-Making applications in Supply Chain Management: Review & Practical cases.- 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: State of the art.- 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.