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Editorial Cuvillier

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Approaches to Enhance the Performance of Simheuristic Methods in the Optimisation of Multi-echelon Logistics Distribution Networks

Impresion
EUR 89,90

E-Book
EUR 63,90

Approaches to Enhance the Performance of Simheuristic Methods in the Optimisation of Multi-echelon Logistics Distribution Networks (Volumen 3) (Tienda española)

Majsa Ammouriova (Autor)

Previo

Lectura de prueba, PDF (240 KB)
Indice, PDF (79 KB)

ISBN-13 (Impresion) 9783736974258
ISBN-13 (E-Book) 9783736964259
Idioma Inglés
Numero de paginas 258
Laminacion de la cubierta mate
Edicion 1
Serie Schriftenreihe Fortschritte in der IT in Produktion und Logistik
Volumen 3
Lugar de publicacion Göttingen
Lugar de la disertacion Dortmund
Fecha de publicacion 11.05.2021
Clasificacion simple Tesis doctoral
Area Estadísticas y operación de investigaciones, Matemática para negocios
Informática
Palabras claves Distributionsnetzwerke, Logistik-Assistenzsysteme, Simheuristiken, Metaheuristiken, Ökonometrie, econometrics, Regressionsanalyse, Paneldaten, Optimierungsverfahren, Logistics Assistance, Enhancement-Ansatz, Standort, Lieferant, Kunde, Lagerhäuser, Stock Keeping Units, Transportbeziehungen, transportation relations, transportation, entities, Entitäten, SKU-Zentralisierung, centralising an SKU, Lagerkosten, Lieferzeit, delivery time, storage costs, customer, suppliers, location, Simulation, Simulationslaufzeit, simulation run time
Descripcion

Management of logistics distribution networks is a challenging task. Decision-makers rely on logistics assistance systems that recommend actions to optimise the networks. These systems can be based on simheuristics to benefit from metaheuristics in exploring possible solutions and on simulation for modelling the networks. This book presents three approaches to recommend promising solutions to optimise the networks with fewer simulation runs. The first approach utilises information from the network to guide the search of metaheuristics. In this approach, domain-specific information is defined and assigned to actions. The metaheuristic algorithm utilises this domain-specific information to find more-promising solutions. The second approach is reducing the number of possible solutions by grouping actions with respect to their domain-specific attributes. Here, the smaller solution space decreases the number of required simulation runs. The last approach looks for equivalent solutions that cause the same changes in the network. This approach aims to skip unnecessary evaluations and, thus, simulation effort.