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dc.contributor.authorBridgelall, Raj
dc.description.abstractThis 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.en_US
dc.publisherMDPIen_US
dc.titleOptimization Problems in Transportation and Logistics: A Practical Guideen_US
dc.typeBooken_US
dc.date.accessioned2024-05-21T18:14:33Z
dc.date.available2024-05-21T18:14:33Z
dc.date.issued2024
dc.identifier.urihttps://hdl.handle.net/10365/33827
dc.subjectspacial optimizationen_US
dc.subjectflow capturingen_US
dc.subjectzone heterogeneityen_US
dc.subjectservice coverageen_US
dc.subjectdecision variablesen_US
dc.subjectgeographic information systems (GIS)en_US
dc.subjectwarehouse location optimizationen_US
dc.identifier.citationBridgelall, R. Optimization Problems in Transportation and Logistics: A Practical Guide; MDPI: Basel, Switzerland, 2024; https://doi.org/10.3390/books978-3-7258-0697-3en_US
dc.language.isoen_USen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/


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