A bi-objective routing problem for cooperated trucks and drones in river water quality monitoring

Abstract

The preservation of urban river ecosystems constitutes a fundamental component in sustainable water resource management. Effective water quality evaluation relies on systematic sampling approaches that encompass diverse aquatic environments. Traditional vessel-based sampling methods are often time-consuming, labor-intensive, and pose a risk of contamination to the aquatic ecosystem. In contrast, the cooperation between ground vehicles (referred to as trucks) and drones offers an efficient and environmentally friendly sampling method. This paper presents a novel approach to water sampling in which drones serve as the primary tools for collecting water samples while trucks extend the flight range of drones by acting as mobile depots. A Mixed Integer Quadratic Programming (MIQP) model is formulated which considers the influence of payload variations of drones on energy consumption during the sampling process. To synchronize the routing of drones and trucks, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is developed, incorporating an iterative front optimization strategy to enhance the solution diversity. Furthermore, three specialized genetic operators tailored to the specific problem scenario are designed to improve the quality of the population. The practicality and efficiency of the proposed algorithm are validated using real-world case studies, highlighting the transformative potential of truck-drone cooperation in urban river water quality monitoring.