This study systematically addresses the coupled decision-making challenges of charging station site selection, dynamic path planning, and multi-objective conflicts in drone logistics networks by constructing a three-dimensional collaborative optimization framework: energy, path, and goal.

This study breaks away from the traditional compartmentalized modeling paradigm and innovatively integrates charging station site selection variables, drone path networks, and multi-objective weights into a unified decision space, constructing a two-level mixed integer programming model.

In this paper, set within a three-dimensional (3D) context and accounting for uncertainties related to UAV positioning errors and ground base station corrections, a multi-objective mixed integer linear programming (MILP) model was developed, incorporating probability constraints.

An algorithm combining variable neighborhood search (VNS) with mixed integer linear programming (MILP) is proposed to solve the proposed model with high efficiency. Finally, the effectiveness and robustness of the proposed solution algorithm are demonstrated by simulation results.

It constructs a delivery network topology, optimizes charging station placement via an improved K-means algorithm, dynamically adjusts routes through Variable Neighborhood Search (VNS) operations, and uses a Mixed Integer Linear Programming (MILP) model to integrate spatio-temporal constraints.

This project has addressed these challenges by developing a drone delivery system that optimizes the location of the battery charging stations, generates the shortest delivery route in the modeled grid, and handles the scheduling of multiple drones operating across multiple delivery locations

In this paper, the joint optimization problem of UAVs path planning, charging scheduling, and VNF deployment is formulated as a Mixed-Integer Non-Linear Programming problem. We have proposed a joint optimization scheme for multi-agent collaboration, which can be divided into two stages.

To address these issues, this study focuses on the integrated optimization of UAV routing and charging station placement in large-scale delivery networks. A location-first-route-second collaborative framework is proposed, which combines a three-level location-routing-charging optimization model.

An algorithm combining variable neighborhood search (VNS) with mixed integer linear programming (MILP) is proposed to solve the proposed model with high efficiency. Finally, the effectiveness and robustness of the proposed solution algorithm are demonstrated by simulation results.

This research proposes a multi-objective mixed integer programming model (MILP) that transforms dynamic uncertainties into binary constraints, utilizing a hierarchical sequencing strategy in the Gurobi optimizer to efficiently identify optimal paths.