No abstract

Delivery route optimization is a crucial concern in the logistics industry, affecting delivery times, costs, and customer satisfaction. The conventional methods for optimizing delivery routes are time-consuming and require substantial manual efforts. To address these limitations, they have increasingly used machine learning algorithms for more efficient and effective optimization. This paper reviews modern techniques for delivery route optimization using machine learning algorithms, including the key challenges faced by delivery companies. Metaheuristic methods, reinforcement learning, and machine learning are discussed, along with their advantages and limitations. In developing a delivery route optimization system, factors such as the number of vehicles, their capacity, delivery time windows, road networks, and customer demand are considered. Different optimization objectives, such as minimizing delivery time, reducing transportation costs, and maximizing customer satisfaction, are presented. Finally, the paper highlights future research directions, including multi-agent systems, swarm intelligence, and hybrid algorithms. This paper provides a comprehensive review of delivery route optimization using machine learning algorithms and can be useful for practitioners and researchers in the logistics industry.

The aim of this paper is to compare online and offline retail price optimization and highlight the key differences. Online retail price optimization uses algorithms and data analysis to set the best price for an item on an e-commerce platform, considering product demand and competition. Offline retail price optimization involves manual methods, such as cost-plus pricing, market pricing, and psychological pricing, to price items in physical stores. The study involved a review of existing literature on retail price optimization and its application in online and offline retailing. The results showed that data availability is a significant difference between online and offline retail price optimization, with online retailers having access to more data. Online retailers can quickly adjust prices because of automation, while offline retailers need to manually change prices. The results of the study emphasize the importance of price optimization in both online and offline retailing and the benefits of using both methods together. The findings provide valuable insights for retail businesses and can inform future research in retail price optimization.