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With the increasing number of airports and the expansion of their scale, the aviation network has become complex and hierarchical. In order to investigate the complex network characteristics of aviation networks, this paper constructs a Chinese aviation network model and carries out related research based on complex network theory and K-means algorithm. Initially, the P-space model is employed to construct the Chinese aviation network model. Then, complex network indicators such as degree, clustering coefficient, average path length, betweenness and coreness are selected to investigate the complex characteristics and hierarchical features of aviation networks and explore their causes. Secondly, using K-means clustering algorithm, five values are obtained as the initial clustering parameter K values for each of the aviation network hierarchies classified according to five complex network indicators. Meanwhile, clustering simulation experiments are conducted to obtain the visual clustering results of Chinese aviation network nodes under different K values, as well as silhouette coefficients for evaluating the clustering effect of each indicator in order to obtain the hierarchical classification of aviation networks under different indicators. Finally, the silhouette coefficient is optimal when the K value is 4. Thus, the clustering results of the four layers of the aviation network can be obtained. According to the experimental results, the complex network association discovery method combined with K-means algorithm has better applicability and simplicity, while the accuracy is improved. [ABSTRACT FROM AUTHOR]

In this paper, the problem of data bundling from different channels to determine the current location of a military aircraft belonging to the platform navigation system was investigated. The calculation of the data bundling coefficients by the least-squares method is proposed based on the data related to loads, orientation angles and speed recorded in the black box during the flight. Data from different channels (speed channel, load channel and GPS channel) are converted to like quantities (expressed as speed). The bundling coefficients (weight coefficients) of the speed and load channels data are calculated based on the GPS data of the flight start time (approximately 30-60 seconds of flight). Using these weight coefficients, the speed and load channels data are bundled, and the trajectory of the aircraft over the entire duration of the flight is plotted. This approach allows obtaining a satisfactory flight path in real-time for the subsequent flight period. [ABSTRACT FROM AUTHOR]

Collaborative decision making (CDM) is a joint Federal Aviation Administration (FAA)/ industry initiative aimed at improving traffic flow management when inclement weather reduces an airport's arrival capacity. CDM replaces the present Ground Delay Program and is expected to be fully implemented by or around the year 2000. Under CDM, during periods of undersupply, the FAA's role shifts from centralized decision-maker to information gatherer and resource arbiter. Filling the decision-making void are the airlines, now given the freedom to make rescheduling decisions according to their own priorities and objectives. In this paper, we present an integer model and a real-time solution algorithm that assist an airline in making these rescheduling decisions at its hub airport, the location with the largest number of operations and therefore the greatest opportunity for improvement. Our research improves the existing state-of-the-art by representing the real world more thoroughly and intuitively than existing models (a modeling contribution) and by exploiting the structure of our model to achieve optimal solutions to large-scale scenarios in real time (an algorithmic contribution). Furthermore, we present four different formulations of the model. Although the different formulations are equivalent in that they have identical integer feasible solution sets and optimal objective function values, they exhibit widely-varying optimization times when tested on large-scale scenarios, allowing us to compare the characteristics and desirability of the alternative formulation techniques. [ABSTRACT FROM AUTHOR]

This paper studies the competitive choice of flight schedules and route prices by airlines operating in a hub-and-spoke system. Airlines choose flight schedules and route prices to maximize profit, considering competitors' decisions. This research makes three contributions. First, an expression is derived calculating demand for each route as a function of the service quality and prices of all routes. Second, a mathematical programming heuristic is developed to find the schedule and prices that maximize an airline's profit against fixed schedules and prices for other airlines. Third, the heuristic is used to study competition by allowing each airline to optimize its schedule and prices against the others' choices and by searching for an equilibrium. The performance of the algorithm is evaluated against alternate heuristics and a five-city sample problem is presented. Finally, two competitive examples are presented and analyzed. [ABSTRACT FROM AUTHOR]

Hub location problem is further used in transportation and telecommunication networks (airlines, post delivery services, etc.) so origin-destination pairs, receive or send commodities via special facilities called Hub. Hub median problem with multiple allocation is an NP-hard problem which includes both locating hub facilities and allocating non-hub nodes to hubs as minimizes total transportation and location costs. In this paper, the hub median problem is considered in an environment which network flow varies during the time periods and the capacities of hubs and arcs are unlimited. Also opening and closing hubs in different periods of planning horizon are permitted. The model and the proposed algorithm for this problem were considered to Iran airlines network using real passenger flows data. Computational results state that the dynamic network compared with static model has lower cost and whatever the number of time periods in dynamic case increases, the cost will be reduced as well. [ABSTRACT FROM AUTHOR]

The efficient operation of airports, and runways in particular, is critical to the throughput of the air transportation system as a whole. Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations of efficiency, safety, and equity among airlines. One approach to runway scheduling that arises from operational and fairness considerations is that of constrained position shifting (CPS), which requires that an aircraft's position in the optimized sequence not deviate significantly from its position in the first-come-first-served sequence. This paper presents a class of scalable dynamic programming algorithms for runway scheduling under constrained position shifting and other system constraints. The results from a prototype implementation, which is fast enough to be used in real time, are also presented. [ABSTRACT FROM AUTHOR]

The black and white traveling salesman problem (BWTSP) is defined on a graph G whose vertex set is partitioned into black and white vertices. The aim is to design a shortest Hamiltonian tour on G subject to cardinality and length constraints: both the number of white vertices as well as the length of the tour between two consecutive black vertices are bounded above. The BWTSP has applications in airline scheduling and in telecommunications. This paper proposes an integer linear formulation for the undirected BWTSP, as well as several classes of valid inequalities. An exact branch-and-cut algorithm is then developed. Extensive tests show that it can solve exactly instances involving up to 100 vertices. The algorithm can also be applied directly to solve unit demand vehicle routing problems of similar sizes. [ABSTRACT FROM AUTHOR]

Congestion in air traffic networks is a serious problem and has received a lot of attention both from the aviation authorities (Federal Aviation Administration, Eurocontrol, etc.) and from the scientific research community. In past years, one way of reducing the amount of congestion has been the adoption of ground holding policies, i.e., a ground hold is imposed to selected aircraft, prior to their departure, so that congestion may be smoothed away. Many airlines in the United States have been complaining about these policies and are pushing toward the new concept of "free flight," where the airlines are almost "free" to choose for each of their flights, when to depart, which route to follow, at what speed, etc., as long as the arrival at the destination airport matches a given time, decided by a central authority (the Federal Aviation Administration in the United States). In this new perspective, to avoid congestion, or at least to reduce it, the central authority has to schedule the arrival times of all flights, with possible delays for some of them. In this paper, we describe an exact algorithm, based on the integration of a heuristic algorithm with an integer linear programming model. This approach provides exact solutions in a much shorter computation time than previous algorithms proposed in the literature. We will report on our computational experiences using large instances based on actual Official Airline Guide data for the United States air traffic network. [ABSTRACT FROM AUTHOR]

As the airline industry is ever expanding, companies are increasing their fleet sizes to obtain greater market shares. Moreover, as the airlines seek more growth, the complexity and size of the airline crew scheduling problem, which is one of the major planning problems in the industry, is also increasing. For this reason, companies dedicate resources to solve this problem, and lease software at great expense from external sources. Rigorous mathematical models and algorithms are used in solving these problems. This paper presents a survey of airline crew scheduling problems, and their proposed solutions from the literature. As a conclusion, prospective studies will be proposed and discussed with the aim of developing better solutions for airline crew scheduling problems in the future. [ABSTRACT FROM AUTHOR]

In the last decade, new advances in algorithms have been as important as the impressive advances in computer technology. Using the new interior-point algorithms and advanced implementations of simplex methods, we can now solve linear programs with more than one million variables and thousands of constraints. Preprocessing and polyhedral theory have yielded at least an order of magnitude improvement in branch-and-bound algorithms for solving mixed integer programs. Moreover, these algorithmic advances have been incorporated in commercially inexpensive software that is readily available, easily portable, and supported by a variety of systems that make it possible for unsophisticated users to input and check their models and obtain understandable outputs. This paper, based on the Morse Lecture given in May 1993 at the TIMS/ORSA meeting in Chicago, begins with some of the modern history of optimization, then surveys some recent developments (illustrating them with an application in the airline industry), and closes with some remarks about the future. [ABSTRACT FROM AUTHOR]

To enhance the accuracy of performance analysis of regional airline network, this study applies complex network theory and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to investigate the topology of regional airline network, constructs node importance index system, and clusters 161 airport nodes of regional airline network. Besides, entropy power method and approximating ideal solution method (TOPSIS) is applied to comprehensively evaluate the importance of airport nodes and complete the classification of nodes and identification of key points; adopt network efficiency, maximum connectivity subgraph and network connectivity as vulnerability measurement indexes, and observe the changes of vulnerability indexes of key nodes under deliberate attacks and 137 nodes under random attacks. The results demonstrate that the decreasing trend of the maximum connectivity subgraph indicator is slower and the decreasing trend of the network efficiency and connectivity indicators is faster when the critical nodes of the regional airline network are deliberately attacked. Besides, the decreasing trend of the network efficiency indicator is faster and the decreasing trend of the maximum connectivity subgraph indicator is slower when the nodes of four different categories are randomly attacked. Finally, it is proposed to identify and focus on protecting critical nodes in order to better improve the security level of regional airline system. [ABSTRACT FROM AUTHOR]

Aircraft emissions are the main cause of airport air pollution. One of the keys to achieving airport energy conservation and emission reduction is to optimize aircraft taxiing paths. The traditional optimization method based on the shortest taxi time is to model the aircraft under the assumption of uniform speed taxiing. Although it is easy to solve, it does not take into account the change of the velocity profile when the aircraft turns. In view of this, this paper comprehensively considered the aircraft's taxiing distance, the number of large steering times and collision avoidance in the taxi, and established a path optimization model for aircraft taxiing at airport surface with the shortest total taxi time as the target. The genetic algorithm was used to solve the model. The experimental results show that the total fuel consumption and emissions of the aircraft are reduced by 35% and 46%, respectively, before optimization, and the taxi time is greatly reduced, which effectively avoids the taxiing conflict and reduces the pollutant emissions during the taxiing phase. Compared with traditional optimization methods that do not consider turning factors, energy saving and emission reduction effects are more significant. The proposed method is faster than other complex algorithms considering multiple factors, and has higher practical application value. It is expected to be applied in the more accurate airport surface real-time running trajectory optimization in the future. Future research will increase the actual interference factors of the airport, comprehensively analyze the actual situation of the airport's inbound and outbound flights, dynamically adjust the taxiing path of the aircraft and maintain the real-time performance of the system, and further optimize the algorithm to improve the performance of the algorithm. [ABSTRACT FROM AUTHOR]

PoW (Proof of Work) plays a significant role in most blockchain systems to grant an accounting right over decentralized participants and ensure tamper resistance. Though hash functions are generally exploited for PoW due to their merits on summering, anti-collision, and irreversibility, they cannot certify that the bookkeeper is exactly the worker. Thereafter, such insistence may lead to abuse or even embezzlement of computing power for the benefit of malicious miners. To preserve the functionality of PoW but also bind the miners' signing keys with their works, we build a post-quantum PoW scheme by changing the approximate closest vector norm for probabilistic NTRUSign. Different from the schemes based on hash functions, our scheme takes signing as the proof of work where signature verification is just the evidence of block reward. We also presented a method to adjust the difficulty of signing by modifying the probability of generating a correct signature. The performance of our scheme is also analyzed theoretically and experimentally, which implies its practicability and advantages. [ABSTRACT FROM AUTHOR]

A bundle algorithm is presented to solve a multicommodity network model for determining a recovery plan for a single carrier with multiple fleets following a hub closure. The algorithm is shown to provide feasible near-optimal solutions much more quickly than can be obtained using a standard commercial mixed-integer programming code (CPLEX). In this application, a bundle method is used to solve a Lagrangian relaxation of the integer programming formulation. The full algorithm includes heuristic techniques for finding feasible solutions from the solutions to the relaxed problems. Extensive computations were performed using data from Continental Airlines. The results show that the proposed approach provides faster times to optimality in some cases and always obtains feasible, near-optimal solutions for larger problems much more quickly than can be found using CPLEX. In addition, while a standard commercial code will provide only one solution, this approach provides multiple high-quality solutions. [ABSTRACT FROM AUTHOR]

In this paper, we present a computerized procedure for scheduling airline crews. The procedure performs set partitioning on a graph by decomposing the problem into a series of subproblems which are solved as matching problems. Computational tests of the algorithm reveal consistent performance in producing efficient solutions to the air crew scheduling problem. [ABSTRACT FROM AUTHOR]

In this paper, we develop a stochastic, multiclass network equilibrium model of airline passenger transportation. The model explicitly incorporates the behavior of the passengers in regard to the decision whether or not to travel, and route selection, and formalizes the passenger's decision as an abstract network. The equilibrium between the realized demand for the routes of travel and the supply of the seats is shown to satisfy a system of nonlinear equations. A Gauss-Seidel algorithm is then proposed for the computation of the equilibrium and conditions for convergence are established. Finally, the algorithm is applied to compute the solution to the national Air Canada airline passenger network, and the output is compared to the actual observed flows. [ABSTRACT FROM AUTHOR]

It is common practice for airlines to charge several different fares for a common pool of scats. This paper presents a model that has been used to address the problem of when to refuse booking requests for a given fare level to save the seat for a potential request at a higher fare level. This model assumes that lower fare passengers book before higher fare passengers book. This occurs quite frequently in practice, where lower fare passengers are usually vacationers and higher fare passengers are usually business travelers. In fact, many airlines mandate this practice by imposing advance booking requirements on lower fare classes to prevent business travelers from using them, We present an algorithm that finds a seat management policy that maximizes mean revenue by establishing a critical value for each fare class. Booking requests for a particular fare level are accepted if the number of empty seats is strictly greater than its critical value and rejected otherwise. This critical value is a decreasing function of the fare price and is equal to zero for the class with the highest fare. [ABSTRACT FROM AUTHOR]

Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers' next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. We then compare the performance of the Next Likely Destination model in a real-life consumer study with 35,000 actual airline customers. In the user study, the model obtains a 51% predictive accuracy. What happened? The Individual Behavior Framework theory provides insights into possibly explaining this inconsistency in evaluation outcomes. Research results indicate that algorithmic approaches in competitive industries must account for shifting customer preferences, changes to the travel environment, and confounding business effects rather than relying solely on historical data. [ABSTRACT FROM AUTHOR]

Airline schedule planning problems are typically decomposed into smaller problems, which are solved in a sequential manner, due to the complexity of the overall problems. This results in suboptimal solutions as well as feasibility issues in the consecutive phases. In this study, we address the integrated fleet assignment and crew pairing problem (IFACPP) of a European Airline. The specific network and cost structures allow us to develop novel approaches to this integrated problem. We propose an optimization-driven algorithm that can efficiently handle large scale instances of the IFACPP. We perform a computational study on real-world monthly flight schedules to test the performance of our solution method. Based on the results on instances with up to 27,500 flight legs, we show that our algorithm provides solutions with significant cost savings over the sequential approach. [ABSTRACT FROM AUTHOR]

As data and supercomputing centres increase their performance to improve service quality and target more ambitious challenges every day, their carbon footprint also continues to grow, and has already reached the magnitude of the aviation industry. Also, high power consumptions are building up to a remarkable bottleneck for the expansion of these infrastructures in economic terms due to the unavailability of sufficient energy sources. A substantial part of the problem is caused by current energy consumptions of High Performance Computing (HPC) clusters. To alleviate this situation, we present in this work EECluster, a tool that integrates with multiple open-source Resource Management Systems to significantly reduce the carbon footprint of clusters by improving their energy efficiency. EECluster implements a dynamic power management mechanism based on Computational Intelligence techniques by learning a set of rules through multi-criteria evolutionary algorithms. This approach enables cluster operators to find the optimal balance between a reduction in the cluster energy consumptions, service quality, and number of reconfigurations. Experimental studies using both synthetic and actual workloads from a real world cluster support the adoption of this tool to reduce the carbon footprint of HPC clusters. [ABSTRACT FROM AUTHOR]

Airline selection is a multi-criteria decision-making (MCDM) problem faced in business travel. The evaluation process primarily involves the evaluation of several complex factors for selecting an airline that will best meet business travel needs. Previous studies have proposed several different methods that a company or organization can use to evaluate or select an airline. In its proposed approach to solving airline selection, this study integrates the analytical hierarchy process (AHP) with a type of preference ordering involving the determination a solution's similarity to an ideal solution (TOPSIS) and multi-segment goal programming (MSGP). A real-life case study on selecting an airline is also presented. [ABSTRACT FROM AUTHOR]

Airline crew scheduling algorithms widely used in practice assume no disruptions. Because disruptions often occur, the actual cost of the resulting crew schedules is often greater. We consider algorithms for finding crew schedules that perform well in practice. The deterministic crew scheduling model is an approximation of crew scheduling under uncertainty with the assumption that all pairings will operate as planned. We seek better approximate solution methods for crew scheduling under uncertainty that still remain tractable. We give computational results from three fleets that indicate that the crew schedules obtained from our method perform better in a model of operations with disruptions than the crew schedules found via deterministic methods. Under mild assumptions we provide a lower bound on the cost of an optimal crew schedule in operations, and we demonstrate that some of the crew schedules found using our method perform very well relative to this lower bound. [ABSTRACT FROM AUTHOR]

Solving LP relaxations of airline crew scheduling models is computationally challenging due to the presence of a large number of variables, complex feasibility rules to generate columns, and nonlinear cost. We perform computational experiments with a nonlinear pricing strategy. We develop a column generation scheme that uses several pruning rules to fathom column enumeration. The pruning rules are categorized into approximate and exact, where the approximate rules might prune columns that would yield an improved objective value. The pruning rules use the fact that columns are paths in a network, and we use shortest path algorithms and their extensions to obtain bounds. [ABSTRACT FROM AUTHOR]

The Wedelin algorithm is a Lagrangian based heuristic that is being successfully used by Carmen Systems to solve large crew pairing problems within the airline industry. We extend the Wedelin approach by developing an implementation for personnel scheduling problems (also termed staff rostering problems) that exploits the special structure of these problems. We also introduce elastic constraint branching with the twin aims of improving the performance of our new approach and making it more column generation friendly. Numerical results show that our approach can outperform the commercial solver CPLEX on difficult commercial rostering problems. [ABSTRACT FROM AUTHOR]

The airline crew scheduling problem is the problem of assigning crew itineraries to flights. The problem is hard due to a large number of itineraries and the complicated cost function of an itinerary. The classic approach, which uses a set partitioning formulation, only captures the objective function of minimizing cost. We add a second goal of maximizing the repetition or regularity of itineraries over a weekly horizon and we develop models that capture regularity. We present a new algorithm that solves the crew scheduling problem over a weekly horizon. Solutions are reported that improve on existing solutions used by an airline. [ABSTRACT FROM AUTHOR]

We investigate a simple adaptive approach to optimizing seat protection levels in airline revenue management systems. The approach uses only historical observations of the relative frequencies of certain seat-filling events to guide direct adjustments of the seat protection levels in accordance with the optimality conditions of Brumelle and McGill (1993). Stochastic approximation theory is used to prove the convergence of this adaptive algorithm to the optimal protection levels. In a simulation study, we compare the revenue performance of this adaptive approach to a more traditional method that combines a censored forecasting method with a common seat allocation heuristic (EMSR-b). [ABSTRACT FROM AUTHOR]