Byung Duk Song

On the Scheduling of Systems of UAVs and Fuel Service Stations for Long-Term Mission Fulfillment

The duration of missions that can be accomplished by a system of unmanned aerial vehicles (UAVs) is limited by the battery or fuel capacity of its constituent UAVs. However, a system of UAVs that is supported by automated refueling stations may support long term or even indefinite duration missions. We develop a mixed integer linear program (MILP) model to formalize the problem of scheduling a system of UAVs and multiple shared bases in disparate geographic locations. There are mission trajectories that must be followed by at least one UAV. A UAV may hand off the mission to another in order to return to base for fuel. To address the computational complexity of the MILP formulation, we develop a genetic algorithm to find feasible solutions when a state-of-the-art solver such as CPLEX cannot. In practice, the approach allows for a long-term mission to receive uninterrupted UAV service by successively handing off the task to replacement UAVs served by geographically distributed shared bases.

Persistent UAV Service: An Improved Scheduling Formulation and Prototypes of System Components

The flight duration of unmanned aerial vehicles (UAVs) is limited by their battery or fuel capacity. As a consequence, the duration of missions that can be pursued by UAVs without supporting logistics is restricted. However, a system of UAVs that is supported by automated logistics structures, such as fuel service stations and orchestration algorithms, may pursue missions of conceivably indefinite duration. This may be accomplished by handing off the mission tasks to fully fueled replacement UAVs when the current fleet grows weary. The drained UAVs then seek replenishment from nearby logistics support facilities. To support the vision of a persistent fleet of UAVs pursuing missions across a field of operations, we develop an improved mixed integer linear programming (MILP) model that can serve to support the system’s efforts to orchestrate the operations of numerous UAVs, missions and logistics facilities. Further, we look toward the future implementation of such a persistent fleet outdoors and develop prototype components required for such a system. In particular, we develop and demonstrate the concerted operation of a scheduling model, UAV onboard vision-based guidance system and replenishment stations.

Rolling Horizon Path Planning of an Autonomous System of UAVs for Persistent Cooperative Service: MILP Formulation and Efficient Heuristics

A networked system consisting of unmanned aerial vehicles (UAVs), automated logistic service stations (LSSs), customer interface software, system orchestration algorithms and UAV control software can be exploited to provide persistent service to its customers. With efficient algorithms for UAV task planning, the UAVs can autonomously serve the customers in real time. Nearly uninterrupted customer service may be accomplished via the cooperative hand-off of customer tasks from weary UAVs to ones that have recently been replenished at an LSS. With the goal of enabling the autonomy of the task planning tasks, we develop a mixed integer linear programming (MILP) formulation for the problem of providing simultaneous. UAV escort service to multiple customers across a field of operations with multiple sharable LSSs. This MILP model provides a formal representation of our problem and enables use in a rolling horizon planner via allowance of arbitrary UAV initial locations and consumable reservoir status (e.g., battery level). As such, it enables automation of the orchestration of system activities. To address computational complexity, we develop efficient heuristics to rapidly derive near optimal solutions. A receding horizon task assignment (RHTA) heuristic and sequential task assignment heuristic (STAH) are developed. STAH exploits properties observed in optimal solutions obtained for small problems via CPLEX. Numerical studies suggest that RHTA and STAH are 45 and 2100 times faster than solving the MILP via CPLEX, respectively. Both heuristics perform well relative to the optimal solution obtained via CPLEX. An example demonstrating the use of the approach for rolling horizon planning is provided.

The design of capacitated facility networks for long term care service

Current research results are extended by allowing more than one type of facilities.Also, the capacity of facilities is considered with closest assignment requirement.A branch and bound algorithm is developed for exact solution with pruning rules, lower bound and upper bound.A genetic algorithm is presented for solving large sized problem. Life expectancy is going up and the demand of long term care facilities is increasing in most countries. This study deals with designing problem of facility networks for long-term care services in a city consisting of a number of regions. Assuming that in each region a candidate site for long-term care facility exists, we seek to identify regions where opening of a long-term care facility is desirable and also determine the type of new facility. For the problem, an integer programming model is formulated with the objective of minimizing the total construction cost. The closest assignment rule is adopted to reflect the preference of patient in choosing long term care facility by assigning patient to an open facility closest from his home. To solve the model, we develop a branch and bound algorithm for exact solution and a genetic algorithm to solve large sized problem. The validity of the mathematical model and the proposed algorithms are illustrated through a number of problem instances.

Towards real time scheduling for persistent UAV service: A rolling horizon MILP approach, RHTA and the STAH heuristic

The automation of logistics tasks for fleets of UAVs is a key element of persistent operation. Such automation includes the provision of robotic service stations to replace consumables and orchestration algorithms enabling the UAVs to simultaneously pursue their objectives and manage the logistics process. Here we consider a system of UAVs and service stations distributed across a field of operations whose purpose is to provide continuous escort/surveillance to customers traversing known time-space trajectories. Our goal is to develop centralized real-time large scale-system orchestration methods for such a service. This goal is pursued in three directions. 1)We extend an existing mixed integer linear program (MILP) formulation to allow for arbitrary UAV initial locations and fuel levels. The MILP uses a more general service station recharge model. The new MILP is incorporated into a rolling horizon optimization for real time use. 2) We extend an RHTA heuristic to allow for arbitrary fuel levels and UAV locations. 3) Based on insight from the problem formulation, the STAH heuristic is developed. Numerical studies assess the effectiveness and numerical character of the proposed approaches. STAH was at least 30 times faster than RHTA with similar values. Both are much faster than the MILP solved via CPLEX. A real time scheduling example is considered.

Persistent UAV service: An improved scheduling formulation and prototypes of system components

The flight duration of unmanned aerial vehicles (UAVs) is limited by their battery or fuel capacity. As a consequence, the duration of missions that can be pursued by UAVs without supporting logistics is restricted. However, a system of UAVs that is supported by automated logistics structures, such as fuel service stations and orchestration algorithms, may pursue missions of conceivably indefinite duration. This may be accomplished by handing off the mission tasks to fully fueled replacement UAVs when the current fleet grows weary. The drained UAVs then seek replenishment from nearby logistics support facilities. To support the vision of a persistent fleet of UAVs pursuing missions across a field of operations, we develop an improved mixed integer linear programming (MILP) model that can serve to support the systems efforts to orchestrate the operations of numerous UAVs, missions and logistics facilities. Further, we look toward the future implementation of such a persistent fleet out-doors and develop prototype components required for such a system. In particular, we develop and demonstrate the concerted operation of a scheduling model, UAV onboard vision-based guidance system and replenishment stations.

Location, capacity and capability design of emergency medical centers with multiple emergency diseases

Mathematical model was developed to support design of emergency medical system.Location, capability and capacity of emergency medical center were designed.To ensure the survival of patients, minimum survival rate was applied.As an alternative solution procedure, hybrid genetic algorithm was developed.Deterministic solution was derived and validated via simulation studies. This paper studies the location, allocation and capacity design of emergency medical centers (EMC) in a given region under the closest assignment rule. It is assumed that for each EMC candidate center the capability and initial capacity on each category of treatable medical diseases are provided. Selected medical center receives a prescribed amount of subsidies from the government in return for the offering of medical services at a competitive cost. It is further assumed that with additional subsidies EMC candidate center can not only enlarge its capacities for treatable medical diseases but also newly begin medical treatment for the diseases not included in the original capability. The number of patients of each patient group node during a unit time is assumed to be known along with the categories of their diseases. The problem is formulated as an integer program (IP) with the objective of minimizing the total amount of subsidies paid by the government. We select from among a set of candidate EMCs satisfying minimum desired survival rate constraints and determine both the capability and capacity of each EMC. The CPLEX version 12.4 is used to derive an optimal solution. Hybrid genetic algorithm was developed as a solution procedure for generating near-optimal solution. In addition, simulation studies are conducted to evaluate the performance of the proposed deterministic models in a stochastic context.