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    Optimization Problems in Transportation and Logistics

    A Practical Guide

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    Author(s)
    Bridgelall, Raj
    Language
    English
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    Abstract
    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.
    URI
    https://library.oapen.org/handle/20.500.12657/92381
    Keywords
    spatial optimization; flow capturing; zone heterogeneity; service coverage; decision variables; geographic information systems (GIS); warehouse location optimization
    DOI
    10.3390/books978-3-7258-0697-3
    ISBN
    9783725806980
    Publisher
    MDPI - Multidisciplinary Digital Publishing Institute
    Publication date and place
    Basel, 2024
    Classification
    Computer science
    Mathematics
    Pages
    68
    Rights
    All rights reserved
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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