Revolutionizing International Cargo Transportation: A Data-Driven Strategy for Fleet Management Optimization and Workforce Efficiency

Tämä opinnäytetyö laajentaa suomalaisen kuljetusyrityksen logistisia tarpeita, jotka lupaavat asiakkailleen yhden päivän toimituksen. Reitin suunnittelu ja rahdin jakelu on ollut suunnittelijoille työvaltainen tehtävä, sillä suunnitelmaa rakennetaan eri puolilta Eurooppaa Helsingin satamaan ja jatke...

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Päätekijä: Tahir, Muhammad Adeel
Muut tekijät: Faculty of Information Technology, Informaatioteknologian tiedekunta, Jyväskylän yliopisto, University of Jyväskylä
Aineistotyyppi: Pro gradu
Kieli:eng
Julkaistu: 2024
Aiheet:
Linkit: https://jyx.jyu.fi/handle/123456789/95343
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author Tahir, Muhammad Adeel
author2 Faculty of Information Technology Informaatioteknologian tiedekunta Jyväskylän yliopisto University of Jyväskylä
author_facet Tahir, Muhammad Adeel Faculty of Information Technology Informaatioteknologian tiedekunta Jyväskylän yliopisto University of Jyväskylä Tahir, Muhammad Adeel Faculty of Information Technology Informaatioteknologian tiedekunta Jyväskylän yliopisto University of Jyväskylä
author_sort Tahir, Muhammad Adeel
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description Tämä opinnäytetyö laajentaa suomalaisen kuljetusyrityksen logistisia tarpeita, jotka lupaavat asiakkailleen yhden päivän toimituksen. Reitin suunnittelu ja rahdin jakelu on ollut suunnittelijoille työvaltainen tehtävä, sillä suunnitelmaa rakennetaan eri puolilta Eurooppaa Helsingin satamaan ja jatketaan rekoilla maanteitse. Tämän opinnäytetyön päätavoitteena on automatisoida tämä suunnitteluprosessi vähentämällä käsityötä ja käyttämällä ML/AI- tekniikoita järjestelmän tehostamiseksi mukautuvammaksi ja muuttuvammaksi. Historiallisia tietoja kerätään luomalla ORM ei-relaatiotietokannan ympärille tärkeiden tietojen poimimiseksi, joita tarvitsemme aiempien vuorovaikutusten visualisoimiseksi ja automaatiosuunnitelman laatimiseksi. OpenStreetMaps ja mukautettu lastinjakoalgoritmi on muotoiltu toimimaan yhtenäisellä tavalla. Lastin jakaminen kontteihin on yhdistetty reittisuunnitelmaan, jolla lämpötilaherkät tuotteet jakautuvat oikein. Kuljetussuunnittelijat saavat graafisen käyttöliittymän vuorovaikutukseen suunnitelman kanssa ja siihen oleellisten muutosten tekemiseen. Suunnitelma tallennetaan ja käsitellään koneoppimis- ja tekoälyalgoritmin kouluttamiseksi. Deep Q-Networkiä käytetään piilotettujen parametrien käsittelemiseen iteratiivisessa takaisinkytkentäsilmukassa painojen integroimiseksi takaisin optimoituun järjestelmään. This thesis expands on the logistic needs of the Finnish transportation company that prom- ises their client single day delivery. Route planning and Cargo distribution has been a labor- intensive task for the planners as a plan is constructed from all over Europe to Helsinki harbor and continuing on truck by road. The main objective of this thesis is to automate this process of planning by reducing manual labor and use ML/AI techniques to enhance the system to be more adaptive and resilient to the changes. Historical data is collected from creating an ORM around the non-relational database to ex- tract essential information we need to visualize the past interactions and devise a plan for automation. OpenStreetMaps and custom cargo distribution algorithm is formulated to work in a unified manner. Cargo allocation into the containers is coupled with a route plan to properly distribute the temperature sensitive products. Transport planners are provided with a graphical user-interface to interact with the plan and make essential changes to it, which is being stored and processed to train the machine learning and artificial intelligence algorithm. Deep Q-Network is used to handle hidden parameters in an iterative feedback loop to inte- grate weights back into the optimized system.
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spellingShingle Tahir, Muhammad Adeel Revolutionizing International Cargo Transportation: A Data-Driven Strategy for Fleet Management Optimization and Workforce Efficiency Mathematical Information Technology Tietotekniikka
title Revolutionizing International Cargo Transportation: A Data-Driven Strategy for Fleet Management Optimization and Workforce Efficiency
title_full Revolutionizing International Cargo Transportation: A Data-Driven Strategy for Fleet Management Optimization and Workforce Efficiency
title_fullStr Revolutionizing International Cargo Transportation: A Data-Driven Strategy for Fleet Management Optimization and Workforce Efficiency Revolutionizing International Cargo Transportation: A Data-Driven Strategy for Fleet Management Optimization and Workforce Efficiency
title_full_unstemmed Revolutionizing International Cargo Transportation: A Data-Driven Strategy for Fleet Management Optimization and Workforce Efficiency Revolutionizing International Cargo Transportation: A Data-Driven Strategy for Fleet Management Optimization and Workforce Efficiency
title_short Revolutionizing International Cargo Transportation: A Data-Driven Strategy for Fleet Management Optimization and Workforce Efficiency
title_sort revolutionizing international cargo transportation a data driven strategy for fleet management optimization and workforce efficiency
title_txtP Revolutionizing International Cargo Transportation: A Data-Driven Strategy for Fleet Management Optimization and Workforce Efficiency
topic Mathematical Information Technology Tietotekniikka
topic_facet Mathematical Information Technology Tietotekniikka
url https://jyx.jyu.fi/handle/123456789/95343 http://www.urn.fi/URN:NBN:fi:jyu-202405304106
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