Automated Route Optimization and Delivery Scheduling Using AI Algorithms

EasyChair Preprint 13217

Abstract

Automated route optimization and delivery scheduling using AI algorithms is a rapidly growing field that aims to enhance the efficiency and cost-effectiveness of logistics operations. Traditional manual planning methods often struggle to handle the complexities of modern supply chains, resulting in suboptimal routes and inefficient delivery schedules. By harnessing the power of AI algorithms, organizations can optimize their routes and schedules to minimize transportation costs, improve delivery times, and enhance customer satisfaction.

This abstract provides an overview of the key concepts and benefits associated with automated route optimization and delivery scheduling using AI algorithms. It highlights the importance of data collection and preparation, including the integration of real-time data sources, to ensure accurate and up-to-date information for analysis. Various AI algorithms commonly employed for route optimization, such as Genetic Algorithms, Ant Colony Optimization, and Reinforcement Learning, are discussed, along with their respective strengths and complexities.

Similarly, the abstract explores AI algorithms used for delivery scheduling, such as Constraint Programming, Tabu Search, and Machine Learning techniques. These algorithms consider factors such as time windows, customer preferences, and traffic conditions to generate optimal delivery schedules. The integration of real-time data, including GPS, traffic information, and weather updates, enables dynamic adjustments and re-optimization to adapt to changing conditions.

Keyphrases: Automated route optimization, Deployment, case studies, delivery scheduling, implementation

@booklet{EasyChair:13217,
  author    = {Godwin Olaoye and Elizabeth Henry},
  title     = {Automated Route Optimization and Delivery Scheduling Using AI Algorithms},
  howpublished = {EasyChair Preprint 13217},
  year      = {EasyChair, 2024}}