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LAST-MILE DELIVERY SCHEDULING USING AUTONOMOUS DELIVERY ROBOTS

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Date Issued:
2022
Abstract/Description:
Urban freight system constitutes an essential component for both economic and social aspects of the urban areas. However, the driving forces of globalization and ecommerce have adversely affected the volume of freight vehicles in urban roads over the past few decades impacting the sustainability and efficiency of last-mile deliveries. At the same time, the last-mile problem of goods distribution from companies to customers comprises one of the most costly and highest polluting components of the supply chain. Over the past few years, different innovative concepts of autonomous vehicles were introduced to improve last-mile logistic inefficiencies such as traffic congestion and pollution externalities. The objective of this study is to optimize a distribution network consisting of a set of depots and customers by utilizing Autonomous Delivery Robots (ADRs). For that reason, a Mixed Integer Linear Programming model was developed in GAMS for solving the vehicle routing problem while minimizing the total delivery and delay costs of ADRs. This optimization model is based on the route assignment and the required number of ADRs within the network. A heuristic solution algorithm based on the cluster-first, route-second technique was developed in MATLAB for solving the NP-hard problem efficiently. First the customers were clustered to depots based on their maximum distance from them and the maximum allowed number of customers per cluster. After the clustering, the mathematical model was implemented in each cluster providing an exact solution. Three different medium-sized scenarios of 200, 300 and 400 customers were tested under three different clustering instances of a maximum of 20, 30 and 40 customers per cluster and their results were presented and discussed in detail.
Title: LAST-MILE DELIVERY SCHEDULING USING AUTONOMOUS DELIVERY ROBOTS.
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Name(s): Antonoglou, Vasileia, author
Kaisar, Evangelos I. , Thesis advisor
Florida Atlantic University, Degree grantor
Department of Civil, Environmental and Geomatics Engineering
College of Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2022
Date Issued: 2022
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 88 p.
Language(s): English
Abstract/Description: Urban freight system constitutes an essential component for both economic and social aspects of the urban areas. However, the driving forces of globalization and ecommerce have adversely affected the volume of freight vehicles in urban roads over the past few decades impacting the sustainability and efficiency of last-mile deliveries. At the same time, the last-mile problem of goods distribution from companies to customers comprises one of the most costly and highest polluting components of the supply chain. Over the past few years, different innovative concepts of autonomous vehicles were introduced to improve last-mile logistic inefficiencies such as traffic congestion and pollution externalities. The objective of this study is to optimize a distribution network consisting of a set of depots and customers by utilizing Autonomous Delivery Robots (ADRs). For that reason, a Mixed Integer Linear Programming model was developed in GAMS for solving the vehicle routing problem while minimizing the total delivery and delay costs of ADRs. This optimization model is based on the route assignment and the required number of ADRs within the network. A heuristic solution algorithm based on the cluster-first, route-second technique was developed in MATLAB for solving the NP-hard problem efficiently. First the customers were clustered to depots based on their maximum distance from them and the maximum allowed number of customers per cluster. After the clustering, the mathematical model was implemented in each cluster providing an exact solution. Three different medium-sized scenarios of 200, 300 and 400 customers were tested under three different clustering instances of a maximum of 20, 30 and 40 customers per cluster and their results were presented and discussed in detail.
Identifier: FA00013978 (IID)
Degree granted: Thesis (MS)--Florida Atlantic University, 2022.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Freight and freightage
Robotics
Urban transportation
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00013978
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.