Airport take‐off and landing optimization through genetic algorithms

Chapter 5 - Nonquadratic Penalty Functions — Convex Programming

Duality in Markov decision processes (MDPs) has been studied recently by several researchers with the goal to derive dual bounds on the value function. In this paper we propose the idea of using parameterized penalty functions in the dual representation of MDPs, which allows us to integrate different types of penalty functions and guarantees a tighter dual bound with more penalties used. To complement and diversify the existing linear penalties developed in the literature, we also introduce a new class of nonlinear penalties that can be used for a broad class of problems and are also easy to implement in practice. Based on this new class of penalties, our framework of parameterized penalties is a promising method to produce tighter dual bounds than existing duality-based methods. We compare the performance of the dual bounds induced by different penalties on a numerical example, demonstrating the effectiveness of our method.

Bearing in mind the socio-economic consequences of COVID-19 on airports and passengers, this study considers cleaning and hygiene a top priority in airports and airlines’ operating processes. Both airport operators and passengers should be aware that the COVID-19 pathogen has not yet gone away and, thus, continues to spread despite mass vaccination. In this context, this paper’s objective is to show the importance of cleaning and hygiene in the air transport industry to encourage the passengers’ confidence in making decisions to travel on commercial airlines and help airport and airline operators make better decisions in their efforts to improve the cleaning and hygiene protocols. Passengers’ awareness of hygiene and cleanliness has increased, but unfortunately, airports and airlines have reduced cleaning and hygiene activities to save costs. Although there is much to do to help the aviation sector recover, we also see plenty of reasons to be optimistic about the air transport industry’s future, as long as airport and airline operators can guarantee an adequate quality of service with regard to cleaning and hygiene in order to ensure passengers’ safety and well-being. Airports and airlines cannot lower their guard because the SARS, H1N1, MERS, and COVID-19 viruses remain active in society.

Extensive research in predicting annual passenger throughput has been conducted, aiming at providing decision support for airport construction, aircraft procurement, resource management, flight scheduling, etc. However, how airport operational throughput is affected by convective weather in the vicinity of the airport and how to predict short-term airport operational throughput have not been well studied. Convective weather near the airport could make arrivals miss their positions in the arrival stream and reduce airfield efficiency in terms of the utilization of runway capacities. This research leverages the learning-based method (MB-ResNet model) to predict airport hourly throughput and takes Hartsfield–Jackson Atlanta International Airport (ATL) as the case study to demonstrate the developed method. To indicate convective weather, this research uses Rapid Refresh model (RAP) data from the National Oceanic and Atmospheric Administration (NOAA). Although it is a comprehensive and powerful weather data product, RAP has not been widely used in aviation research. This study demonstrated that RAP data, after being carefully decoded, cleaned, and pre-processed, can play a significant role in explaining airfield efficiency variation. Applying machine learning/deep learning in air traffic management is an area worthy of the attention of aviation researchers. Such advanced artificial intelligence techniques can make use of big data from the aviation sector and improve the predictability of the national airspace system and, consequently, operational efficiency. The short-term airport operational throughput predicted in this study can be used by air traffic controllers and airport managers for the allocations of resources at airports to improve airport operations.

Understanding and managing de-icer contamination of airport surface waters: A synthesis and future perspectives

Airport facility management requires innovative and coordinated techniques due to the infrastructure’s complexity, stakeholders’ diversity, and the necessity of safety. Adopting building information management (BIM) as an advanced technology has several benefits, including increased productivity, lower cost, and higher quality of service. This study seeks to determine the strategies for using BIM in airport facility management. In this vein, two questionnaires were developed to collect data based on a literature review. The first questionnaire was used to collect data for identifying and ranking the main criteria, and the second questionnaire was used to identify the practical strategies. The experts of this study answered five strengths, four weaknesses, five opportunities, and five threats using a standardized questionnaire. An integrated AHP-SWOT approach was used to identify and examine the practical strategies. Furthermore, a sensitivity analysis was used to ensure the results were correct. The findings showed that smart maintenance management, with a weight of 0.363, was the most important strength in the SWOT analysis. Resistance to change was the most important weakness, with a weight of 0.455. The increasing need for smart airports with a weight of 0.358 was the most important opportunity, while cybersecurity issues with a weight of 0.385 were the most important threat. Integrating BIM into the aviation sector can enhance efficiency and sustainability in airport facility management while addressing potential opportunities and shared hazards that extend beyond airport operations.

Due to the COVID-19 pandemic, aviation at the global level is currently facing one of the biggest challenges in its sustainability. The entire philosophy related to airport operations and passenger processing changes due to the fast-spreading nature of this disease. The “new normal” in the aviation sector, primarily at the airport, will provide the passenger a different quality level of service in comparison to what we used to have until now. Following World Health Organization measures, especially those related to social distances, the future focus will be on the implementation of fully automated self-service passenger traffic flows that will change the airport passenger traffic flows’ processing structure. This paper shows current technologies and processes and how this “new normal” will change philosophy related to Industry 4.0 at the airport passenger operations, functionality, and human resource (HR) planning and optimizations.

A Critical Review on Regional Connectivity Scheme of India

Methods. The results are obtained through the application of methods such as comparative analysis, used to clarify the main content of coordination programs for medium- and long-term innovative development in the aviation industry in EU countries and Ukraine; general and specific methods, employed in characterizing the directions of strategic development programs in the aviation industry; structural-functional analysis, applied to identify features and characteristic traits of development programs in the aviation industry in EU countries. Results. The role and significance of the aviation industry in the national and global economy are characterized. It is emphasized that the aviation sector stimulates scientific research and innovation, contributing to the development of new technologies and the improvement of functional features in related industries. It is revealed that in European Union countries, the state plays a key role in the development of the aviation industry through regulation, financing, ensuring safety, fostering innovation, and developing programs that shape a comprehensive set of short-term measures, ensuring the coordinated development of the aviation industry in these countries. It is highlighted that the European aviation industry has achieved significant success in the production and operation of aircraft, air traffic management, and ensuring the highest level of safety over the past decade. The reasons for crisis phenomena in the aviation industry in Ukraine are classified. The necessity of state coordination and support for the innovative development of this industry, which is a strategic sector of the state, is substantiated. It is established that the Ukrainian aviation industry has substantial potential and can be competitive in global aviation markets, as well as meet the needs of the national economy and play a key role in the country's defense capabilities. Novelty. In the course of the research, it is found that state coordination of the aviation industry in the EU and Ukraine differs both in terms of structure and perspectives of development. It is argued that in Ukraine, to overcome the crisis situation and in the face of geopolitical challenges, it is necessary to develop short-, medium-, and long-term programs that are interconnected and ensure innovative development. Practical value. It is proposed to consider the experience of state coordination of the aviation industry in European Union countries when developing a national strategy for the innovative development of the aviation sector, taking into account modern realities and globalization challenges.

Recombination is an important operator in population metaheuristics. Recently, there has been interest in developing efficient recombination operators that explore characteristics of the optimization problem. We propose using a radial basis function network (RBFN) to generate recombination masks based on information contained in the parents and on past successful recombinations. The RBFN is trained online and the training set is built by adding examples of successful recombinations obtained during the optimization process. The time complexity for training the RBFN is O(N^2), while the time complexity for applying the proposed crossover operator is O(N), the same time complexity of traditional crossover operators. Experiments with NK landscapes indicated that a genetic algorithm (GA) with the proposed crossover was capable of generating better results for successful recombination and improvement rates, when compared to a GA with traditional crossover. However, this did not imply in better results for the best fitness.

The study of reactive silencers by shape and parametric optimization techniques

Association Rule mining is very efficient technique for finding strong relation between correlated data. The correlation of data gives meaning full extraction process. For the mining of positive and negative rules, a variety of algorithms are used such as Apriori algorithm and tree based algorithm. A number of algorithms are wonder performance but produce large number of negative association rule and also suffered from multi-scan problem. The idea of this paper is to eliminate these problems and reduce large number of negative rules. Hence we proposed an improved approach to mine interesting positive and negative rules based on genetic and MLMS algorithm. In this method we used a multi-level multiple support of data table as 0 and 1. The divided process reduces the scanning time of database. The proposed algorithm is a combination of MLMS and genetic algorithm. This paper proposed a new algorithm (MIPNAR_GA) for mining interesting positive and negative rule from frequent and infrequent pattern sets. The algorithm is accomplished in to three phases: a).Extract frequent and infrequent pattern sets by using apriori method b).Efficiently generate positive and negative rule. c).Prune redundant rule by applying interesting measures. The process of rule optimization is performed by genetic algorithm and for evaluation of algorithm conducted the real world dataset such as heart disease data and some standard data used from UCI machine learning repository.

Mitigation of aircraft noise pollution is a core goal of managing environmental interactions and inherently aligns with the aim of reducing aviation emissions. As noise pollution adversely affects the operation and expansion of airports by restricting land zoning and flight patterns, developing precise noise generation and propagation models is imperative. Efforts to minimize the impact of aircraft noise are influenced by the accurate mapping of the geometric distribution of that noise. The Federal Aviation Administration (FAA) utilizes the Aviation Environmental Design Tool (AEDT) to model aircraft noise, emissions, and air quality consequences for regulatory compliance and system planning. Aircraft operations and fleet mix data are required when users execute AEDT to compute the noise exposure level. However, such data are difficult to obtain from non-towered airports that lack full-time air traffic facilities and personnel. Several airport operations estimation approaches developed by researchers have shown limitations in accuracy and cost-efficiency of deployment when tested, limiting their usefulness with regards to noise modeling. Meanwhile, tracking aircraft through onboard transponder data was validated as a cost-effective approach to estimate aircraft operations at non-towered airports.The authors designed a platform consisting of three modules. Flight operations and aircraft performance parameters can be estimated in the first module by deploying inexpensive hardware to collect aircraft transponder signals. The second module estimates aircraft noise levels by integrating information sources with the noise vs. power vs. distance (NPD) data from the EUROCONTROL Aircraft Noise and Performance (ANP) database. The third module visualizes airport noise impact based on a Geographic Information System (GIS) platform. Additionally, a risk assessment and a sustainability analysis are presented in this paper.

The main issue for enhancing the productivity in Printed Circuit Board (PCB) is to reduce the cycle time for pick and place (PAP) operations; i.e., to minimize the time for the PAP operations. According to the characteristics of the PAP problems, the sequence for the placement of components can be mostly treated as the Travelling Salesman Problem (TSP). In this paper, a Genetic Algorithm (GA) with External Self-evolving Multiple Archives (ESMA) is developed for minimizing the PAP operations in PCB assembly line. ESMA focuses on the issue of improving the premature convergence time in GA by adopting efficient measures for population diversity, effective diversity control and mutation strategies to enhance the global searching ability. Three mechanisms for varietal GA such as Clustering Strategy, Switchable Mutation and Elitist Propagation have been designed based on the concept of increasing the dynamic diversity of the population. The experimental results in PCB and TSP instances show that the proposed approach is very promising and it contains the ability of local and global searching. The experimental results show ESMA can further improve the performance of GA by searching the solution space with more promising results.

Purpose: To develop an artificial intelligence based generative design framework to optimize radiation shielding for satellites and spacecraft using a genetic algorithm combined with a generative adversarial network and compare the performance to using only a genetic algorithm. Methods: The optimization framework is developed and tested on three different models, a simple cube, the SpaceX Dragon vehicle, and the STPSat-4 satellite. Each model's geometry is created in our Monte Carlo particle transport code with an added allowed shielding volume. Each model is exposed to a simulated radiation source using the Monte Carlo code. We use a genetic algorithm, GA, where the material distribution is represented by individuals in the algorithm. GAs are evolutionary algorithms primarily used to perform optimizations. The goal of the optimization is to minimize the dose to a specified volume inside the vehicle. Halfway through the optimization, the results are used to train a generative adversarial network, GAN, which is a type of generative model based on adversarial training. The GAN then produces new shielding solutions that are fed into the optimization process. The shielding solutions produced by the GAN-assisted-GA are compared to those produced by the GA only, and both are compared to the reference baseline of aluminum shielding. Results: Our optimization framework was able to reduce the internal dose by up to 20% for the generic satellite, 14% for the Dragon vehicle, and up to 45% for the STPSat-4 for equal weights of uniform aluminum. We found the GAN-assisted-GA was able to improve the overall optimization performance by 6% for the simple cube and 8% for the Dragon vehicle. Conclusions: Our genetic algorithm based optimization framework was able to produce radiation shielding profiles with reduced dose compared to aluminum references of the same weight. We also showed increased performance by incorporating a generative adversarial network into the genetic algorithm optimization process. This work demonstrates the ability of our approach to be used to design radiation shielding for spacecraft and satellites.