Research on Arrival/Departure Scheduling of Flights on Multirunways Based on Genetic Algorithm

For slotted random access systems, the slotted ALOHA protocol provides the maximum throughput of 0.368 (packets/slot) while in the category of splitting (or tree) algorithms, the maximum achievable throughput can reach up to 0.487 with the first-come first-serve (FCFS) algorithm. It has been so far demonstrated that the FCFS algorithm can achieve this maximum throughput only for Poisson traffic. This may limit its application in practical systems, where packet arrivals may not be Poissonian. In this paper, we propose a novel online transmission control framework that introduces random splitting upon collisions and controls the transmission probabilities optimally at each slot by estimating the number of active users in the system. The proposed algorithm is said to be online as it estimates the number of active users slot by slot recursively, and thus can adapt to network dynamics. We first show that the splitting algorithm of our interest can achieve the throughput of 0.532 if the number of users involved in a collision could be known, which serves as a guideline for the upper limit for the random access systems with splitting. Then, when the information on the number of collided users is not available, we show that the proposed algorithm can achieve the maximum throughput of 0.487 for Poisson arrivals while achieving shorter access delay than FCFS. When more bursty traffic than Poisson process is applied, the proposed algorithm shows much better throughput and delay performance than FCFS.

Aircraft are subject to steady deterioration effects in flight operations resulting in increased fuel consumption due to decreased engine efficiency, increased airframe surface roughness and increased operating empty mass. These effects are taken into account in operational flight planning (e.g. fuel calculations) but long-term efficiency loss is rarely respected in flight and rotation scheduling. In most cases, each aircraft type per fleet series is treated homogeneously and scheduled considering maintenance events, equipment and other restrictions. This work investigates fuel saving potentials by additionally considering the aircraft tail sign-specific efficiency during aircraft to flight assignment. For this purpose, we calculate additional fuel consumption caused by deterioration effects, expressed as performance factor and mass deviation. By using an integrated flight scheduling and aircraft rotation model, aircraft will be reassigned to flights from given flight schedules by minimizing operating costs or fuel consumption. Our research yields in a saving of 22,400 kg fuel for 15 long-haul aircraft (150 kg per reassigned flight on average) based on a flight schedule for a typical week of an air cargo operator. The saving potential depends on the number of flight legs, their distance and the number of available aircraft, their performance variation and loading factor. Although relative savings are of theoretical nature and marginal to total fuel burn (0.2%), we propose an inexpensive approach to decrease total fuel consumption. Furthermore, we show that the usual approach of assigning less efficient aircraft to short flight legs is applicable but does not always provide in the cost minimum solution because low cruising altitudes of short-haul flights adversely affect fuel consumption.

The fusion of artificial fish algorithm and genetic algorithm is proposed to optimize the flight scheduling of two-runway in large airport in which the problems are difficult, inefficient and serious flight delays. First, in this paper, the shortcomings of artificial fish algorithm and genetic algorithm are analyzed, and combing the advantages of two algorithms, optimization algorithm is proposed based on the fusion of artificial fish swarm algorithm and genetic algorithm; Secondly, the objective function model of the total of time-consuming which is minimum about dual runway is established; Finally, the simulation results show that the fusion algorithm in operating efficiency, the reduction of flight delays and other aspects is better than first-come, first-served (FCFS), artificial fish algorithm and genetic algorithm, and can get better convergence results.

Integrated development and operational collaboration of regional airport groups have the potential to improve capacity and safety and also reduce environmental impacts and operational costs. However, research in multiairport systems (MASs), especially in China, is still in its infancy, with the consequences of unbalanced development, inadequate coordination, unclear function partitioning, difficulty in air traffic management, and poor service quality of regional airports. Considering these characteristics influencing effective interaction and collaboration of regional airports, this paper formulates a model to optimize the flight schedules in the MAS with multiple objectives of minimizing the maximum displacement of all flights, the weighted sum of total flight adjustment of each airport, and flight delays. An improved simulated annealing algorithm (SAA) is designed to solve the proposed multiobjective optimization problem. The model is applied to a case of the Beijing-Tianjin-Hebei Airport Group. The computational results demonstrate that the model generates significant reductions in maximum displacement, average displacement, and average delay, compared to the First-Come-First-Served (FCFS) principle. The model proposed in this paper can be used by civil aviation authorities, air navigation service providers, and airlines to facilitate the integrated management of flight schedules in the MAS.

The most important mode of long‐distance transportation for people to use time efficiently is air transportation. One of the most significant factors determining air traffic performance is the scheduling created by companies. When creating schedules, various parameters are used, mainly the demand for the determined route, and the availability of airplanes and flight crews. Known climatic conditions on the route are generally neglected. Within the scope of this study, the creation of flight schedules, which are normally the preparation of flight schedules by considering the monthly, daily, and hourly constraints that could cause flight delays, were determined by also considering weather conditions. In this determination process, 32‐year flight data between 1987 and 2018 were used as a basis. Apache Spark, one of the big data technologies, was used to determine these constraints. The scheduling was optimized by using the constraints used to prevent flight delays. It was observed in the results obtained that simulated annealing (SA) achieved the most optimal results compared to the genetic algorithm (GA) and the artificial bee colony (ABC) algorithm. As the dimensions of the searched space increased, solving the problem with metaheuristic approaches was more advantageous in terms of time compared to the classical method.

The escalation in air traffic demand has precipitated the establishment of the multi-airport systems aimed at effectively handling the influx of flights. Nevertheless, these multi-airport systems encounter airspace resource conflicts during the departure phase, rendering resolution challenging when airports operate on separate schedules. Collaborative departure scheduling has been identified as a promising and effective solution to address these challenges. However, the collaborative departure scheduling presents inherent complexity as an NP-hard problem. In sight of this, this paper proposes a two-step optimization framework to address this typical NP-hard optimization problem. In the first step, the take-off time of each flight is optimized in the first step, so as to minimize flight delays and scheduling chaos. Subsequently, the framework proceeds to optimize the standard instrument departure (SID) based on the optimal take-off time. A multi-agent reinforcement learning (MARL) approach is implemented in both of the two steps to optimize take-off times and select SIDs, respectively. The trained model can quickly collaborate departure scheduling in various situations, reducing flight delays, scheduling chaos, and departure distances compared to solutions generated by First Come First Served (FCFS) principle. The proposed framework and approach are tested on real air traffic data from the Beijing-Tianjin-Hebei region in Northern China. The results show that the MARL-based collaborative departure scheduling has promising applications in multi-airport systems and offers a scalable, adaptive, and efficient solution to this complex problem.

In day-to-day operations, flight delays might happen due to some reason such as bad weather conditions, equipment failure, and limited runway capacity. Flight delays do not only increase crew operating cost, they can cause delay propagation on subsequent flights. Since the crew cost is the second largest cost, it is important to construct a robust flight schedule where any flight delay will not lead to a massive disruption to the subsequent downstream flights. In this paper, we analyze the effect of flight retiming to the robustness of flight schedule. We construct a simulation for measuring the robustness of the flight schedule, where the probability of crew pairing found after a number of flight delays happen is used as the measure of robustness. It is shown that the retiming will increase the robustness of the schedule if we only retime the flights which appear in the first cycle of the set of selected pairings.

Airlines’ expensive resources, especially aircraft, are to be optimally scheduled to cover flights of timetables. However, the irregular flight, due to bad weather, mechanical fault and so on, is inevitable. Moreover, flight delays become more severe with the rapid development of the air transport industry in China and have huge irregular flight cost. In order to alleviate flight delays impact on the flight plan, we present a double objective multi-commodity network flow model of flight delay propagation-based aircraft scheduling and minimize the total delay propagation and airline operation cost as the optimization objective. Branch-and-price solution and column generation algorithm are used to solve the problem. Computational results obtained by using data from a major domestic airline show that our approach can reduce delay propagation significantly, thus improving on-time performance and robustness of aircraft scheduling, and decreasing the total cost simultaneously.

Accurate flight delay prediction contains great reference value for airline business and passenger travel. Recent studies have been concentrated on applying machine learning methods to predict the probability of flight delay. Most of the previous prediction methods are built for a single air route or airport. This paper explores a broader spectrum of factors that may potentially affect the flight delay and proposes a gradient boosting decision tree (GBDT) based models for generalized flight delay prediction. To build a dataset for the proposed model, automatic dependent surveillance-broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition of airport, flight schedule, and airport information. Since the delay prediction results can be given with higher resolution, the designed prediction tasks contain four different classification tasks. Experimental results show that the proposed GBDT-based model can obtain higher prediction accuracy (87.72% for the binary classification) when handling our limited dataset.

An investigation was conducted concerning the problem of flight delays to propose a solution to enhance the robustness of airline flight schedules. The robustness of flight schedules depended on the ability to reduce the effects of flight delays on daily operations. The approach to achieve this objective was optimizing flight schedules by allocating the optimal amount of slack to scheduled connections. Identifying appropriate slack between two successive flights involved modeling primary delay distributions at departure airports, considering both time intervals and delay lengths. A threshold approach was subsequently devised to determine the optimal slack based on this distribution. The ideal allocation of slack to schedules was achieved by modifying flight departure times, a technique known as re-timing. Recognizing that the optimal allocation of slack could make schedules infeasible, an optimization model was established for adjusting flight departure times. The proposed model produced new schedules that optimized all slack. The practical application of this model to the schedules of Indonesian airlines demonstrated the method's efficiency in improving the robustness of flight schedules.

Heuristic Approach to the Airline Schedule Disturbances Problem: Single Fleet Case

Airport scheduling has an important optimization value in air transportation, and its complexity and multiple constraints make it difficult for existing methods to deal with it comprehensively. This paper is a review paper, that systematically summarizes the application of genetic algorithms and machine learning in the scheduling of flights, the scheduling of airport resource allocation, and the scheduling of airport external resource allocation. Through the analysis of many studies, this paper summarizes the advantages and disadvantages of different algorithms in various scheduling scenarios and points out the advantages of genetic algorithms in dealing with complex multi-objective optimization problems. In addition, this paper explores the potential of combining machine learning with genetic algorithms to further enhance the effect of scheduling optimization. This paper provides an important reference for future research and helps the innovation and practical application of methods in the field of airport scheduling.

Limiting flight delays during operations has become a critical research topic in recent years due to their prohibitive impact on airlines, airports, and passengers. A popular strategy for addressing this problem considers the uncertainty of day-of-operations delays and adjusts flight schedules to accommodate them in the planning stage. In this work, we present a stochastic programming model to account for uncertain future delays by adding buffers to flight turnaround times in a controlled manner. Specifically, our model adds slack to flight connection times with the objective of minimizing the expected value of the total propagated flight delay in a schedule. We also present a parallel solution framework that integrates an outer approximation decomposition method and column generation. Further, we demonstrate the scalability of our approach and its effectiveness in reducing delays with an extensive simulation study of five different flight networks using real-world data.

The relationship between airport congestion and passenger delays at an airport is determined by many factors. Among these factors are scheduled and actual flight times, flight itineraries, airport capacity, and turn around time between flights. From the existing recorded data, it is possible to present a general profile of both airport congestion and passenger delays, measured with respect to the published schedule, at major airports within the US. This paper illustrates the discrepancy between the two measurements. Airport congestion may be masked by flight schedules which account for routine arrival delays and/or with slack time provided for turn around at an airport. It is possible for an airport to be heavily utilized, with little or unnoticeable at-gate delays (measured with respect to scheduled arrival time). On the other hand, at-gate delays can occur at under-utilized airports. Arrival and departure queuing delays are better indicators of airport congestion than passenger delays. Average flight at-gate delays are derived from performance data and compared with simulation results for 30 of the busiest airports in USA for a good weather day. The effects of variations in schedule flight time and slack time are studied parametrically in the simulation and calibrated to match the recorded baseline national airspace system (NAS) delay profiles. This paper illustrates a technique for estimating NAS-wide impact of propagated flight delays, based upon a method for calibration a system-level simulation against real world delays as well as delays derived from other simulations.

Integrated flight scheduling and fleet assignment with improved supply-demand interactions