國立虎尾科技大學 |

Enhancing Delivery Logistics Using Blockchain and Machine Learning / Smart Delivery Quest.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Enhancing Delivery Logistics Using Blockchain and Machine Learning / Smart Delivery Quest./
Author:	Kandula, LaxmaReddy.
Description:	1 online resource (109 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798383058121

Enhancing Delivery Logistics Using Blockchain and Machine Learning / Smart Delivery Quest.
Kandula, LaxmaReddy.

Enhancing Delivery Logistics Using Blockchain and Machine Learning / Smart Delivery Quest. - 1 online resource (109 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--Southern Illinois University at Carbondale, 2024.

Includes bibliographical references

In the rapidly evolving landscape of delivery logistics, the integration of cutting-edge technologies such as Blockchain, Machine Learning (ML), and Swarm Robotics stands at the forefront of innovation, promising to revolutionize the way businesses manage and execute deliveries. This thesis explores the synergistic potential of these technologies to optimize delivery logistics, ensuring efficiency, security, and reliability in the supply chain. At the heart of our investigation is Machine Learning, which facilitates advanced demand forecasting and dynamic route optimization. Through the analysis of vast datasets encompassing sales, weather, and traffic conditions, ML algorithms predict delivery demands with unprecedented accuracy, enabling logistics companies to allocate resources effectively and navigate complex urban environments optimally.Blockchain technology introduces a layer of transparency and security, particularly in transaction management and data integrity. By leveraging smart contracts, the delivery process is automated, from payment processing to real-time delivery confirmations, fostering trust among all stakeholders and significantly reducing the potential for disputes and fraud.Swarm Robotics, inspired by the collective behavior of natural systems, offers a scalable and flexible solution for the physical execution of deliveries. Through decentralized control and simple local rules, a fleet of autonomous drones or robots collaborates to perform delivery tasks efficiently, adapting to dynamic environmental conditions without central oversight.The combination of these technologies heralds a new era in delivery logistics, where Machine Learning's predictive power, Blockchain's security, and Swarm Robotics' operational efficiency converge to create a robust, adaptable, and future-proof delivery ecosystem. Through theoretical exploration, system design, and empirical analysis, this thesis proposes a comprehensive framework that not only addresses current logistical challenges but also anticipates future developments in the field.This research contributes to the academic and practical understanding of how Blockchain, Machine Learning, and Swarm Robotics can collectively enhance delivery logistics. It offers valuable insights for logistics companies seeking to innovate their operations, policymakers aiming to regulate emerging technologies, and researchers exploring the intersection of technology and supply chain management. Ultimately, this thesis lays the groundwork for a smarter, more connected, and efficient delivery system, paving the way for the seamless integration of technology into the fabric of global commerce.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383058121Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Environmental conditionsIndex Terms--Genre/Form:

554714
Electronic books.

Enhancing Delivery Logistics Using Blockchain and Machine Learning / Smart Delivery Quest.
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