Applied Simulation and Optimization 2 : New Applications in Logistics, Industrial and Aeronautical Practice

Building on the author’s earlier Applied Simulation and Optimization, this book presents novel methods for solving problems in industry, based on hybrid simulation-optimization approaches that combine the advantages of both paradigms. The book serves as a comprehensive guide to tackling scheduling,...

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Corporate Author: SpringerLink (Online service)
Other Authors: Mujica Mota, Miguel (Editor), Flores De La Mota, Idalia (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2017, 2017
Edition:1st ed. 2017
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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250 |a 1st ed. 2017 
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505 0 |a Preface -- Introduction -- Supply Chain Problems -- Logistics Problems -- Manufacturing Problems -- Aeronautical Problems -- Big Data Applications -- Conclusions -- Bibliography -- References 
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653 |a Computational Science and Engineering 
653 |a Industrial engineering 
653 |a Engineering mathematics 
653 |a Computer simulation 
653 |a Mathematical and Computational Engineering 
653 |a Computer mathematics 
653 |a Industrial and Production Engineering 
653 |a Simulation and Modeling 
653 |a Production engineering 
700 1 |a Flores De La Mota, Idalia  |e [editor] 
710 2 |a SpringerLink (Online service) 
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520 |a Building on the author’s earlier Applied Simulation and Optimization, this book presents novel methods for solving problems in industry, based on hybrid simulation-optimization approaches that combine the advantages of both paradigms. The book serves as a comprehensive guide to tackling scheduling, routing problems, resource allocations and other issues in industrial environments, the service industry, production processes, or supply chains and aviation. Logistics, manufacturing and operational problems can either be modelled using optimization techniques or approaches based on simulation methodologies. Optimization techniques have the advantage of performing efficiently when the problems are properly defined, but they are often developed through rigid representations that do not include or accurately represent the stochasticity inherent in real systems. Furthermore, important information is lost during the abstraction process to fit each problem into the optimization technique. On the other hand, simulation approaches possess high description levels, but the optimization is generally performed through sampling of all the possible configurations of the system. The methods explored in this book are of use to researchers and practising engineers in fields ranging from supply chains to the aviation industry.