Display Omitted An improved adaptive PSO based on Alpha-stable distribution and dynamic fractional calculus is studied.A new multi-objective optimization model of gate assignment problem is proposed.The actual data are used to demonstrate the effectiveness of the proposed method. Gate is a key resource in the airport, which can realize rapid and safe docking, ensure the effective connection between flights and improve the capacity and service efficiency of airport. The minimum walking distances of passengers, the minimum idle time variance of each gate, the minimum number of flights at parking apron and the most reasonable utilization of large gates are selected as the optimization objectives, then an efficient multi-objective optimization model of gate assignment problem is proposed in this paper. Then an improved adaptive particle swarm optimization(DOADAPO) algorithm based on making full use of the advantages of Alpha-stable distribution and dynamic fractional calculus is deeply studied. The dynamic fractional calculus with memory characteristic is used to reflect the trajectory information of particle updating in order to improve the convergence speed. The Alpha-stable distribution theory is used to replace the uniform distribution in order to escape from the local minima in a certain probability and improve the global search ability. Next, the DOADAPO algorithm is used to solve the constructed multi-objective optimization model of gate assignment in order to fast and effectively assign the gates to different flights in different time. Finally, the actual flight data in one domestic airport is used to verify the effectiveness of the proposed method. The experiment results show that the DOADAPO algorithm can improve the convergence speed and enhance the local search ability and global search ability, and the multi-objective optimization model of gate assignment can improve the comprehensive service of gate assignment. It can effectively provide a valuable reference for assigning the gates in hub airport.

Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment

AIAA Guidance, Navigation, and Control Conference

Autonomous drones (also known as unmanned aerial vehicles) are increasingly popular for diverse applications of light-weight delivery and as substitutions of manned operations in remote locations. The computing systems for drones are becoming a new venue for research in cyber-physical systems. Autonomous drones require integrated intelligent decision systems to control and manage their flight missions in the absence of human operators. One of the most crucial aspects of drone mission control and management is related to the optimization of battery lifetime. Typical drones are powered by on-board batteries, with limited capacity. But drones are expected to carry out long missions. Thus, a fully automated management system that can optimize the operations of battery-operated autonomous drones to extend their operation time is highly desirable. This paper presents several contributions to automated management systems for battery-operated drones: (1) We conduct empirical studies to model the battery performance of drones, considering various flight scenarios. (2) We study a joint problem of flight mission planning and recharging optimization for drones with an objective to complete a tour mission for a set of sites of interest in the shortest time. This problem captures diverse applications of delivery and remote operations by drones. (3) We present algorithms for solving the problem of flight mission planning and recharging optimization. We implemented our algorithms in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. We evaluated the results of our algorithms using data from empirical studies. (4) To allow fully autonomous recharging of drones, we also develop a robotic charging system prototype that can recharge drones autonomously by our drone management system.

Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones

In order to overcome the slow convergence speed, poor global search ability and difficult designing rotation angle of quantum-inspired evolutionary algorithm (QEA), an improved quantum-inspired cooperative co-evolution algorithm based on combining the strategies of cooperative co-evolution, random rotation direction and Hamming adaptive rotation angle, namely MSQCCEA is proposed, which is employed to propose a new airport gate allocation optimization method in this paper. In the proposed MSQCCEA, the cooperative co-evolution strategy is used to improve the global search capability. The random rotation direction strategy is developed to change the quantum evolution direction from one to two in order to avoid local optimal solution and realize the full search of the solution space. A new Hamming adaptive rotation angle strategy is designed to enable individuals to adaptively adjust the rotation angle according to the difference degree between the individual and the target individual, so as to improve the global search ability and convergence speed. A new airport gate allocation optimization method using MSQCCEA is realized to effectively allocate airport gates to the flights. Finally, the knapsack problem and the actual airport gate allocation problem are used to verify the effectiveness of the proposed MSQCCEA and gate allocation optimization method, respectively. The comparison experiment results demonstrate that the proposed MSQCCEA has faster convergence speed and higher convergence accuracy, and the proposed gate allocation optimization method takes on great potential to make decisions for actual airport management.

An improved quantum-inspired cooperative co-evolution algorithm with muli-strategy and its application

Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control.

This research focuses on airport gates, which are limited resources in the air transportation system. In spite of its importance, ramp congestion has not been considered in traditional research on gate assignment. This research, therefore, proposes a new perspective on the gate assignment problem that accounts for ramp congestion, as well as passenger flow. The proposed gate assignment problem minimizes weighted nominal taxi time and weighted taxi delay, as well as passenger transit time in the terminal. Nominal taxi time and taxi delay are weighted by the number of passengers on board. As a result, the flow in the passenger terminal and on ramps becomes efficient. To get near-optimal solutions within a reasonable amount of time, two metaheuristic algorithms, tabu search and genetic algorithm, are implemented for two ramp configurations. Also, the tradeoff between passenger flow and ramp congestion is analyzed.

Gate Assignment to Minimize Passenger Transit Time and Aircraft Taxi Time

Passengers' experience is becoming a key metric to evaluate the air transportation system's performance. Efficient and robust tools to handle airport operations are needed along with a better understanding of passengers' interests and concerns. Among various airport operations, this paper studies airport gate scheduling for improved passengers' experience. Three objectives accounting for passengers, aircraft, and operation are presented. Trade-offs between these objectives are analyzed, and a balancing objective function is proposed. The results show that the balanced objective can improve the efficiency of traffic flow in passenger terminals and on ramps, as well as the robustness of gate operations.

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Airport Gate Scheduling for Passengers, Aircraft, and Operations

Choice model based analysis of consumer preference for drone delivery service

The ramp area is a complex portion of the airport, often congestion and interactions between aircraft limit ecient ows for arriving and departing aircraft. Aircraft taxiing trac is constrained by minimum aircraft separation, and geometry of airports. In this paper, we present a new approach to assign gate to minimize ramp congestion and propose a simulation model to predict the operation time within the congested ramp. The optimized gate assignment reduces ramp operation time up by 50%. In addition the estimation of ramp operation times is more accurate when applying the optimized gate assignment algorithm.

Assigning Gates by Resolving Physical Conicts

Departure metering reduces congestion by reducing the number of aircraft present on the airport surface at any time while not starving the runway. Because some departing flights are held at gates, there is a possibility that arriving flights cannot access the gates and have to wait until the gates are cleared. This is called a gate conflict. Robust gate assignment is an assignment that minimizes gate conflicts by assigning gates to aircraft to maximize the time gap between two consecutive flights at the same gate; it makes gate assignment robust, but passengers may walk longer to transfer flights. In order to simulate the airport departure process, a queuing model is introduced. The model is calibrated and validated with actual data from New York's LaGuardia Airport (LGA) and a large U.S. hub airport. Then, the model simulates the airport departure process with the current gate assignment and a robust gate assignment to assess the impact of gate assignment on departure metering. The results show that the robust gate assignment reduces the number of gate conflicts caused by departure metering compared with the current gate assignment. Therefore, robust gate assignment can be combined with departure metering to improve operations at congested airports with limited gate resources.

Sang Hyun Kim

Papers

Gate Assignment to Minimize Passenger Transit Time and Aircraft Taxi Time

Airport Gate Scheduling for Passengers, Aircraft, and Operations

Choice model based analysis of consumer preference for drone delivery service

Assigning Gates by Resolving Physical Conicts

Impact of Gate Assignment on Departure Metering