Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Rapid Modelling for Increasing Competitiveness
A Perspective on Two Decades of Rapid Modeling It is an honor for me to be asked to write a foreword to the Proceedings of the 1st Rapid Modeling Conference. In 1987, when I coined the term “Rapid Modeling” to denote queuing modeling of manufacturing systems, I never imagined that two decades later there would be an international conference devoted to this topic! I am delighted to see that there will be around 40 presentations at the conference by leading researchers from aroundthe world, and about half of these presentationsare represented by written papers published in this book. I congratulate the conference organizers and program committee on the success of their efforts to hold the ...
Managing Urban Logistics
Managing Urban Logistics provides new insights based on the most recent research, theories, and developments in technological and ICT solutions, contemporary corporate trends, the re-evaluation of the role of authorities, and much more. The book shows how to manage these complex urban logistics issues using a long term, systemic perspective where urban freight distribution is an integral part of the entire urban mobility system. It examines the convergence points between mass and customized deliveries, thus modeling the decision processes, trade-offs and tolerances behind these processes to enable a more fluid sharing of urban space.Users will find an approach that tackles these issues from ...
Dynamics in Logistics
These proceedings contain research presented at the 6th International Conference on Dynamics in Logistics, held in February 2018.The integration of dynamics within the modeling, planning and control of logistic processes and networks has shown to contribute massively to the improvement of the latter. Moreover, diversification of markets and demand has increased both the complexity and the dynamic changes of problems within the area of logistics. To cope with these challenges, it must become possible to identify, describe and analyze such process changes. Moreover, logistic processes and networks must be revised to be rapidly and flexibly adaptable to continuously changing conditions. This book presents new ideas to solve such problems, offering technological, algorithmic and conceptual improvements. It primarily addresses researchers and practitioners in the field of industrial engineering and logistics.
Logistikmanagement
None
Logistics Management
This book gathers papers presented at the Logistik-Management-Konferenz 2013, which was organized by the VHB Wissenschaftliche Kommission Logistik and held in Bremen, Germany. The papers reflect the current state-of-the-art in logistics and supply chain management, focusing on environmental sustainability in logistics and supply chain network dynamics and control. The target audience primarily consists of researchers and practitioners in the field, but the book may also be beneficial for graduate students.
Supporting Operational and Real-time Planning Tasks of Road Freight Transport with Machine Learning. Guiding the Implementation of Machine Learning Algorithms
World-wide trends such as globalization, demographic shifts, increased customer demands, and shorter product lifecycles present a significant challenge to the road freight transport industry: meeting the growing road freight transport demand economically while striving for sustainability. Artificial intelligence, particularly machine learning, is expected to empower transport planners to incorporate more information and react quicker to the fast-changing decision environment. Hence, using machine learning can lead to more efficient and effective transport planning. However, despite the promising prospects of machine learning in road freight transport planning, both academia and industry struggle to identify and implement suitable use cases to gain a competitive edge. In her dissertation, Sandra Lechtenberg explores how machine learning can enhance decision-making in operational and real-time road freight transport planning. She outlines an implementation guideline, which involves identifying decision tasks in planning processes, assessing their suitability for machine learning, and proposing steps to follow when implementing respective algorithms.
