Optimizing AI and Machine Learning Technologies to Improve Last-Mile Delivery Efficiency in Quick-Commerce

Abstract

This study explores the optimization of Artificial Intelligence and Machine Learning (AI/ML) technologies to enhance efficiency in last-mile delivery within Quick- Commerce industries. Consumer expectations for fast and reliable delivery are increasing;
therefore, it is essential to assess the effectiveness of existing AI/ML technologies. This thesis addresses the key objectives such as assessing the performance of current algorithms, identifying biases in the existing tech stacks, and designing a hybrid AI/ML technology framework tailoring to the unique needs of last-mile delivery in Quick-Commerce. To explore algorithmic strengths and weaknesses, this study deploys a mixed-methods research approach which combines qualitative insights and quantitative analysis. The findings from this study specify the opportunities to optimize and address the biases in AI/ML algorithms that can affect performance and efficiency. The proposed hybrid model addresses the challenges within Quick-Commerce and lays the foundation for future innovation in last-mile delivery. This thesis bridges theoretical trends with practical applications and provides insights for industry professionals to optimize last-mile delivery processes and contributes to the academic discussion on AI/ML optimization in last-mile delivery of Quick-Commerce