Simulation Based Case Studies In Logistics
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Simulation-Based Case Studies in Logistics
Author: Yuri Merkuryev
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-01-13
“Simulation-based Case Studies in Logistics” presents an intensive learning course on the application of simulation as a decision support tool to tackle complex logistic problems. The book describes and illustrates different approaches to developing simulation models at the right abstraction level to be used efficiently by engineers when dealing with strategic, tactical or operational decisions in logistic systems. 11 simulation-based case studies in logistics and supply chain management are discussed, based on the results of applied research, covering application areas such as production logistics, warehousing, transportation, material flow management, and hospital logistics. “Simulation-based Case Studies in Logistics” is an essential text for postgraduate engineering students and researchers working in the area of logistics modeling and simulation.
Agent-Based Modelling of Socio-Technical Systems
Author: Koen H. van Dam
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-10-09
Decision makers in large scale interconnected network systems require simulation models for decision support. The behaviour of these systems is determined by many actors, situated in a dynamic, multi-actor, multi-objective and multi-level environment. How can such systems be modelled and how can the socio-technical complexity be captured? Agent-based modelling is a proven approach to handle this challenge. This book provides a practical introduction to agent-based modelling of socio-technical systems, based on a methodology that has been developed at TU Delft and which has been deployed in a large number of case studies. The book consists of two parts: the first presents the background, theory and methodology as well as practical guidelines and procedures for building models. In the second part this theory is applied to a number of case studies, where for each model the development steps are presented extensively, preparing the reader for creating own models.
Artificial Intelligence and Industrial Applications
This book gathers the refereed proceedings of the Artificial Intelligence and Industrial Applications (A2IA’2020), the first installment of an annual international conference organized by the ENSAM-Meknes at Moulay Ismail University, Morocco. The 30 papers presented here were carefully reviewed and selected from 141 submissions by an international scientific committee. They address various aspects of artificial intelligence such as smart manufacturing, smart maintenance, smart supply chain management, supervised learning, unsupervised learning, reinforcement learning, graph-based and semi-supervised learning, neural networks, deep learning, planning and optimization, and other AI applications. The book is intended for AI experts, offering them a valuable overview of the status quo and a global outlook for the future, with many new and innovative ideas and recent important developments in AI applications, both of a foundational and practical nature. It will also appeal to non-experts who are curious about this timely and important subject.