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Record Nr. |
UNINA9910254279203321 |
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Titolo |
Applied Simulation and Optimization 2 : New Applications in Logistics, Industrial and Aeronautical Practice / / edited by Miguel Mujica Mota, Idalia Flores De La Mota |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XIV, 282 p. 127 illus., 107 illus. in color.) |
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Disciplina |
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Soggetti |
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Computer mathematics |
Applied mathematics |
Engineering mathematics |
Computer simulation |
Industrial engineering |
Production engineering |
Computational Science and Engineering |
Mathematical and Computational Engineering |
Simulation and Modeling |
Industrial and Production Engineering |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Preface -- Introduction -- Supply Chain Problems -- Logistics Problems -- Manufacturing Problems -- Aeronautical Problems -- Big Data Applications -- Conclusions -- Bibliography -- References. |
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Sommario/riassunto |
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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 |
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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. . |
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