ORCID

0009-0004-5398-6659

Keywords

Last-mile delivery, Traffic congestion prediction, machine learning (deep learning), Intelligent transport systems, Dynamic optimization (dynamic routing), Agent-based simulation

Abstract

Supply chain management has become increasingly critical as transportation costs rise and urban delivery systems place additional pressure on already congested networks. Last-mile delivery—the movement of goods from distribution centers to end users—represents the most expensive and operationally complex segment of the logistics process, particularly in underdeveloped megacities characterized by unpredictable traffic, limited infrastructure, and sparse real-time data. This dissertation proposes an integrated framework for optimizing last-mile delivery in such environments by combining social media–based traffic intelligence, machine learning, and deep reinforcement learning. The framework leverages unstructured, real-time data from social media platforms to enhance traffic prediction and incorporates these predictions into a reinforcement learning model that formulates the delivery problem as a Markov Decision Process. This approach enables dynamic routing decisions that adapt to evolving traffic conditions, with the objective of reducing delivery time, operational cost, and fuel consumption. The proposed framework is evaluated through a case study of Bogotá, Colombia, demonstrating its effectiveness in improving routing efficiency and mitigating the impact of urban traffic uncertainty. The results highlight the potential of integrating alternative data sources with adaptive learning techniques to enhance last-mile delivery performance in data-scarce urban environments.

Completion Date

2026

Semester

Spring

Committee Chair

Rabelo, Luis

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering

Format

PDF

Document Type

Thesis

Identifier

DP0053169

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