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Nature Inspired Optimisation for Delivery Problems : From Theory to the Real World / by Neil Urquhart

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Natural Computing Series | Springer eBook CollectionPublisher: Cham : Springer, 2022Copyright date: © 2022Description: 1 Online-Ressource (XVII, 259 Seiten) : IllustrationenISBN:
  • 9783030981082
Subject(s): Additional physical formats: 9783030981075 | 9783030981099 | 9783030981105 | Erscheint auch als: 9783030981075 Druck-Ausgabe | Erscheint auch als: 9783030981099 Druck-Ausgabe | Erscheint auch als: 9783030981105 Druck-AusgabeDOI: DOI: 10.1007/978-3-030-98108-2Online resources: Summary: Part I, Simple Yet Complex Problems -- The Traveling Salesman Problem -- Vehicle Routing Problems (VRPs) -- More Complex VRPs -- Multi-objective Problems -- Part II, Data and Routing -- An Introduction to Geospatial Data -- Routing Algorithms -- Linking to Data Sources -- Visualising Data -- Part III, Real-World Problems -- Food Deliveries in Rural Areas -- Delivering Milk -- Postal Deliveries -- Mobile Workforce Routing -- Urban Logistics.Summary: This book explains classic routing and transportation problems and solutions, before offering insights based on successful real-world solutions. The chapters in Part I introduce and explain the traveling salesperson problem (TSP), vehicle routing problems (VRPs), and multi-objective problems, with an emphasis on heuristic approaches and software engineering aspects. In turn, Part II demonstrates how to exploit geospatial data, routing algorithms, and visualization. In Part III, the above techniques and insights are combined in real-world success stories from domains such as food delivery in rural areas, postal delivery, workforce routing, and urban logistics. The book offers a valuable supporting text for advanced undergraduate and graduate courses and projects in Computer Science, Engineering, Operations Research, and Mathematics. It is accompanied by a repository of source code, allowing readers to try out the algorithms and techniques discussed.PPN: PPN: 1802137742Package identifier: Produktsigel: ZDB-2-SCS | ZDB-2-SEB | ZDB-2-SXCS
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