Smart Delivery Assignment through Machine Learning and the Hungarian Algorithm

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dc.contributor.authorVásconez, Juan Pablo
dc.contributor.authorSchotborgh, Elias
dc.contributor.authorVásconez, Ingrid Nicole
dc.contributor.authorMoya, Viviana
dc.contributor.authorPilco, Andrea
dc.contributor.authorMenéndez, Oswaldo
dc.contributor.authorGuamán-Rivera, Robert
dc.contributor.authorGuevara, Leonardo
dc.date.accessioned2024-07-10T13:41:20Z
dc.date.available2024-07-10T13:41:20Z
dc.date.issued2024-06
dc.descriptionIndexación: Scopus.
dc.description.abstractIntelligent transportation and advanced mobility techniques focus on helping operators to efficiently manage navigation tasks in smart cities, enhancing cost efficiency, increasing security, and reducing costs. Although this field has seen significant advances in developing large-scale monitoring of smart cities, several challenges persist concerning the practical assignment of delivery personnel to customer orders. To address this issue, we propose an architecture to optimize the task assignment problem for delivery personnel. We propose the use of different cost functions obtained with deterministic and machine learning techniques. In particular, we compared the performance of linear and polynomial regression methods to construct different cost functions represented by matrices with orders and delivery people information. Then, we applied the Hungarian optimization algorithm to solve the assignment problem, which optimally assigns delivery personnel and orders. The results demonstrate that when used to estimate distance information, linear regression can reduce estimation errors by up to 568.52 km (1.51%) for our dataset compared to other methods. In contrast, polynomial regression proves effective in constructing a superior cost function based on time information, reducing estimation errors by up to 17,143.41 min (11.59%) compared to alternative methods. The proposed approach aims to enhance delivery personnel allocation within the delivery sector, thereby optimizing the efficiency of this process.
dc.description.urihttps://www-scopus-com.recursosbiblioteca.unab.cl/record/display.uri?eid=2-s2.0-85197163631&origin=resultslist&sort=plf-f&src=s&nlo=&nlr=&nls=&sid=e0561592176c52c7ad670dc1a7a6893a&sot=aff&sdt=cl&cluster=scofreetoread%2c%22all%22%2ct&sl=61&s=AF-ID%28%22Universidad+Andr%c3%a9s+Bello%22+60002636%29+AND+SUBJAREA%28COMP%29&relpos=9&citeCnt=0&searchTerm=
dc.identifier.citationSmart Cities Open Access Volume 7, Issue 3, Pages 1109 - 1125 June 2024
dc.identifier.doi10.3390/smartcities7030047
dc.identifier.issn26246511
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/58395
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights.licenseCC BY 4.0 ATTRIBUTION 4.0 INTERNATIONAL
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHungarian optimization algorithm
dc.subjectmachine learning
dc.subjectregression model
dc.subjectsmart delivery
dc.titleSmart Delivery Assignment through Machine Learning and the Hungarian Algorithm
dc.typeArtículo
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