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Nature Inspired Optimisation for Delivery Problems

Über Nature Inspired Optimisation for Delivery Problems

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 outthe algorithms and techniques discussed.

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  • Sprache:
  • Unbekannt
  • ISBN:
  • 9783030981075
  • Einband:
  • Gebundene Ausgabe
  • Seitenzahl:
  • 280
  • Veröffentlicht:
  • 15 Mai 2022
  • Ausgabe:
  • 22001
  • Abmessungen:
  • 160x21x241 mm.
  • Gewicht:
  • 588 g.
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Beschreibung von Nature Inspired Optimisation for Delivery Problems

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 outthe algorithms and techniques discussed.

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