Main Article Content
In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancements coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. In addition to four other models we are proposing a decomposition-based restricted genetic dominance (DBCDP-NSGA-II) algorithm, which retains viable and non-facilitating solutions in small areas in order to improve the convergence, distribution and diversity of traditional high-dimensional multi-objective fast-dominated genetic sorting Algorithms (NSGA-II).