An Ant Colony Optimization Based Real-time Mobile Application for the Capacitated Vehicle Routing Problem

Abstract

This work focuses on the capacitated vehicle routing problem. In this work, a real-time application is developed using online real-world data for mobile devices have IOS and Android operating systems. The fuzzy c-means clustering algorithm is used to group the demand points and the ant colony optimization algorithm is employed to determine the best route within each group. The customer demand points and distances between these points are obtained via Google Places and Google Directions APIs. The deviations in the route that result from the environmental and road conditions are identified immediately with the help of global positioning system technology allowing the route suggestions to be made. The developed application was evaluated on two datasets for testing. The test results showed that this real-time application can be used to find the optimum route for the capacitated vehicle routing problem and follow the route optimally.

Keywords

• ant colony optimization
• fuzzy c-means
• global positioning system
• mobile devices
• real time application
• vehicle routing problem