Optimization Problems in Transportation and Logistics: A Practical Guide
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...
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| Format: | Online |
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| Idioma: | anglès |
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MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Accés en línia: | ONIX_20240704_9783725806980_41 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-139245 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1392452024-07-04T09:31:52Z Optimization Problems in Transportation and Logistics: A Practical Guide Bridgelall, Raj spatial optimization flow capturing zone heterogeneity service coverage decision variables geographic information systems (GIS) warehouse location optimization thema EDItEUR::U Computing and Information Technology::UY Computer science 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. 2024-07-04T09:31:49Z 2024-07-04T09:31:49Z 2024 book ONIX_20240704_9783725806980_41 9783725806980 9783725806973 https://directory.doabooks.org/handle/20.500.12854/139245 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/mono/9240 https://mdpi.com/books/pdfview/mono/9240 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0697-3 10.3390/books978-3-7258-0697-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725806980 9783725806973 68 open access |
| spellingShingle | spatial optimization flow capturing zone heterogeneity service coverage decision variables geographic information systems (GIS) warehouse location optimization thema EDItEUR::U Computing and Information Technology::UY Computer science Optimization Problems in Transportation and Logistics: A Practical Guide |
| title | Optimization Problems in Transportation and Logistics: A Practical Guide |
| title_full | Optimization Problems in Transportation and Logistics: A Practical Guide |
| title_fullStr | Optimization Problems in Transportation and Logistics: A Practical Guide |
| title_full_unstemmed | Optimization Problems in Transportation and Logistics: A Practical Guide |
| title_short | Optimization Problems in Transportation and Logistics: A Practical Guide |
| title_sort | optimization problems in transportation and logistics a practical guide |
| topic | spatial optimization flow capturing zone heterogeneity service coverage decision variables geographic information systems (GIS) warehouse location optimization thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | spatial optimization flow capturing zone heterogeneity service coverage decision variables geographic information systems (GIS) warehouse location optimization thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20240704_9783725806980_41 |