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Unmanned aerial vehicles (UAVs) are increasingly utilized in global search and rescue efforts, enhancing operational efficiency. In these missions, a coordinated swarm of UAVs is deployed to efficiently cover expansive areas by capturing and analyzing aerial imagery and footage. Rapid coverage is paramount in these scenarios, as swift discovery can mean the difference between life and death for those in peril. This paper focuses on planning the flight paths for multiple UAVs in windy conditions to efficiently cover rectangular search areas in minimal time. We address this challenge by dividing the search area into a grid network and formulating it as a mixed-integer program (MIP). We derive a precise lower bound for the objective function and develop a fast algorithm with a proven capability of finding either the optimal solution or a near-optimal solution with a constant absolute gap to optimality. Notably, as the problem complexity increases, our solution exhibits a diminishing relative optimality gap while maintaining negligible computational costs compared to the MIP approach. The fast execution speed of the algorithms is demonstrated by numerical experiments with area sizes up to 10000 grid cells. 

Unmanned aircraft systems service suppliers adhere to interoperability standards that require unmanned aircraft operators to submit an operational intent, which describes the planned flight path in four-dimensional space. To ensure fairness, the central database follows a first-come, first-served approach, accepting new operational intents as long as they do not conflict with any active ones. However, creating a viable operational intent is challenging due to moving obstacles. This paper introduces an innovative optimization-based procedure to automate the intent filing process. It utilizes a stacked hexagonal tessellation to model the airspace, offering adjustable granularity. Path finding is accomplished using integer programming on the hex grid. The integer program is solved on a grid canvas that includes only necessary cells, striking a balance between computational efficiency and optimality. Simulation experiments demonstrate the procedure’s effectiveness in generating feasible trajectories, even in scenarios with dense, omnidirectional air traffic. This procedure has the potential to become the foundational software core for low-altitude air traffic management systems, providing strategic deconfliction and constraint management services. 

Unmanned aerial vehicles or drones are widely used or proposed to carry out various tasks in low-altitude airspace. To safely integrate drone traffic into congested airspace, the current concept of operations for drone traffic management will reserve a static traffic volume for the whole planned trajectory, which is safe but inefficient. In this paper, we propose a dynamic traffic volume reservation method for the drone traffic management system based on a multiscale A* algorithm. The planning airspace is represented as a multiresolution grid world, where the resolution will be coarse for the area on the far side. Therefore, each drone only needs to reserve a temporary traffic volume along the finest flight path in its local area, which helps release the airspace back to others. Moreover, the multiscale A* can run nearly in real-time due to a much smaller search space, which enables dynamically rolling planning to consider updated information. To handle the infeasible corner cases of the multiscale algorithm, a hybrid strategy is further developed, which can maintain a similar optimal level to the classic A* algorithm while still running nearly in real-time. The presented numerical results support the advantages of the proposed approach. 

We study the problem of detecting abnormal inactivities within a single-occupied household based on smart meter readings. Such abnormal events include immobilizing medical conditions or sudden deaths of elderly or disabled occupants who live alone, the delayed discovery of which poses realistic social concerns as the population ages. Two novel unsupervised learning algorithms are developed and compared: one is based on nested dynamic time warping (DTW) distances and the other based on Mahalanobis distance with problem-specific features. Both algorithms are able to cold-start from limited historical data and perform well without extended parameter tuning. In addition, the algorithms are small profile in terms of data usage and computational need, and thus are suitable for implementation on smart meter hardware. The proposed methods have been thoroughly validated against real data sets with simulated target scenarios and have exhibited satisfactory performance. An implementation scheme on smart meter hardware is also discussed. 

This paper proposes a new mixed-integer programming (MIP) formulation to optimize split rule selection in the decision tree induction process and develops an efficient search algorithm that is able to solve practical instances of the MIP model faster than commercial solvers. The formulation is novel for it directly maximizes the Gini reduction, an effective split selection criterion that has never been modeled in a mathematical program for its nonconvexity. The proposed approach differs from other optimal classification tree models in that it does not attempt to optimize the whole tree; therefore, the flexibility of the recursive partitioning scheme is retained, and the optimization model is more amenable. The approach is implemented in an open-source R package named bsnsing. Benchmarking experiments on 75 open data sets suggest that bsnsing trees are the most capable of discriminating new cases compared with trees trained by other decision tree codes including the rpart, C50, party, and tree packages in R. Compared with other optimal decision tree packages, including DL8.5, OSDT, GOSDT, and indirectly more, bsnsing stands out in its training speed, ease of use, and broader applicability without losing in prediction accuracy. History: Accepted by RamRamesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Science Foundation Division of Civil, MechanicalandManufacturing Innovation [Grant 1944068]. Supplemental Material: Data are available at https://doi.org/10.1287/ijoc.2022.1225 . 

Abstract The p-center location problem in an area is an important yet very difficult problem in location science. The objective is to determine the location of p hubs within a service area so that the distance from any point in the area to its nearest hub is as small as possible. While effective heuristic methods exist for finding good feasible solutions, research work that probes the lower bound of the problem’s objective value is still limited. This paper presents an iterative solution framework along with two optimization-based heuristics for computing and improving the lower bound, which is at the core of the problem’s difficulty. One method obtains the lower bound via solving the discrete version of the Euclidean p-center problem, and the other via solving a relatively easier clustering problem. Both methods have been validated in various test cases, and their performances can serve as a benchmark for future methodological improvements.