Ant colony algorithm to solve a drone routing problem for hazardous waste collection

Waste management issues are affecting the economic and environmental aspects of modern societies. Thus, growing the interest of academic and industrial research and development in optimizing the process of waste management. As these issues greatly impact human health and environmental aspects and impose a threat, hazardous waste management requires even much more attention. The problem studied in this research is a variant of the vehicle routing problem using an unmanned aerial vehicle (UAV). The focus of this research is on planning the routes for waste collection and disposal using a UAV. The aim is to collect all the waste as early as possible respecting two constraints; the maximum flying and load capacities of the UAV. A two-phase approach has been proposed to solve the investigated problem. This approach is a hybridization of a developed heuristic (IMWMTT) and an Ant Colony Optimization (ACO) algorithm. The experimental study showed that the hybrid approach outperforms a recently published heuristic MWMTT for all tested instances of various sizes.