Optimization Problems in Transportation and Logistics: A Practical Guide
Language: English
Published by Mdpi AG, 2024
- Hardcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
AbeBooks seller since March 25, 2015
Condition: New
£ 49.70
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Add to basketItem description from seller
In English.
Seller Inventory # ria9783725806980_new
- Title
- Optimization Problems in Transportation and Logistics: A Practical Guide
- Author
- Bridgelall, Raj
- Publisher
- Mdpi AG
- Publication year
- 2024
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3725806985
- ISBN 13
- 9783725806980
- Item weight
- 478 grams
This educational guide will help students and practitioners seeking to understand the fundamentals and practice of linear programming. The exercises contained within demonstrate how to solve classical optimization problems with an emphasis on spatial analysis in supply chain management and transport logistics. All exercises describe the Python programs and optimization libraries that can be used to solve them. The first chapter introduces key concepts in linear programming and establishes a new cognitive framework to help students and practitioners set up each optimization problem. This cognitive framework organizes the decision variables, constraints, objective function, and variable bounds in a format that allows for direct application to optimization software. The second chapter introduces two types of mobility optimization problems (shortest path in a network and minimum cost tour) in the context of delivery and service planning logistics. The third chapter introduces four types of spatial optimization problems (neighborhood coverage, flow capturing, zone heterogeneity, service coverage) and provides a workflow for visualizing the optimized solutions in maps. The workflow creates decision variables from maps by using the free geographic information systems (GIS) programs QGIS and GeoDA. The fourth chapter introduces three types of spatial logistics
problems (spatial distribution, flow maximization, warehouse location optimization) and demonstrates how to scale the cognitive framework in software to reach solutions. The final chapter summarizes lessons learned and provides insights about how students and practitioners can modify the Python programs and GIS workflows to solve their own optimization problem and
visualize the results.
"Synopsis" may belong to another edition of this title.
Ria Christie Collections
Uxbridge, United Kingdom
AbeBooks seller since March 25, 2015
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