Smart Urban Logistics

Improving Delivery Services by Computational Intelligence
1. Auflage 2022
ISBN: 978-3-031-16703-4
Verlag: Springer International Publishing

Buch, Englisch, 170 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 465 g

Reihe: Fuzzy Management Methods

Last-mile delivery in cities, where the main problems are the traffic situation and ensuring access to customers’ homes while maintaining their privacy, poses a substantial logistical challenge. This book explores how the service area of mobility, especially last-mile delivery, can be improved and smartified. It demonstrates how a design science method and a transdisciplinary approach have been used to create a traffic area analysis tool that can accommodate the uncertainty and incompleteness of geospatial data; a linguistic traffic merging tool; and a customer classifier. In terms of developing the optimization artifacts, the socio-economic and logistical aspects of cities were considered and fuzzy logic and nature-inspired swarm intelligence (fuzzy ant colony optimization) were applied as basic principles. Pursuing a transdisciplinary approach, the book offers both practical know-how from the industry and theoretical findings, making it a valuable asset for researchers and practitioners in the fields of mobility and logistics.

Zielgruppe

Research

Autoren/Hrsg.

Pincay Nieves, Jhonny

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Einkauf, Logistik, Supply-Chain-Management

Inhaltsverzeichnis

Part I. Motivation & Objectives.- Chapter 1. Introduction.- Part II. Theoretical Background.- Chapter 2. Insights into Smart Cities and Smart Logistics.- Chapter 3. Computational Intelligence.- Part III. Applications.- Chapter 4. Fuzzifying Geospatial Traffic Data to Convey Information.- Chapter 5. Ethical Classification of Postal Customers.- Part IV. Framework and Implementation.- Chapter 6. The Fuzzy Ant Routing (FAR) Conceptual Framework.- Chapter 7. The FAR Artifact.- Part V. Conclusions.- Chapter 8. Outlook and Conclusions.

Über Autor(innen)

Jhonny Pincay Nieves earned his Ph.D. in Computer Science at the University of Fribourg, Switzerland. He has worked for Escuela Superior Politécnica del Litoral (ESPOL University) and the Pontificia Universidad Católica del Ecuador (Ecuador) as a lecturer and researcher. He currently works as data scientist at Viasuisse AG in Biel, Switzerland. His research interests include smart cities and logistics, fuzzy logic, recommender systems, and software engineering.