Nature Inspired Optimisation for Delivery Problems

From Theory to the Real World

By:

Neil Urquhart

Format:

Paperback / softback

Series:

Natural Computing Series

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Description

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.

Author Biography:

Neil Urquhart is a lecturer in the School of Computing at Edinburgh Napier University. After qualifying in Computer Science he worked as a software developer and systems administrator. He returned to academia and completed a PhD in 2002 which examined the use of nature inspired techniques and software agents to construct and optimize delivery networks. Since 2002 he has worked as a lecturer at Edinburgh Napier where he is currently the program leader for Computing Science. His main research interests are algorithms, visualization, evolving systems, bioinspired computing, and modeling.

Release date Australia

May 16th, 2023

Author

Neil Urquhart

Audience

Professional & Vocational

Edition

1st ed. 2022

Illustrations

43 Illustrations, color; 32 Illustrations, black and white; XVII, 259 p. 75 illus., 43 illus. in color.

Pages

259

Series

Natural Computing Series

ISBN-13

9783030981105

Product ID

36806527

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