Experimental test of airplane boarding methods

We report the results of an experimental comparison of different airplane boarding methods. This test was conducted in a mock 757 fuselage, located on a Southern California soundstage, with 12 rows of six seats and a single aisle. Five methods were tested using 72 passengers of various ages. We found a significant reduction in the boarding times of optimized methods over traditional methods. These improved methods, if properly implemented, could result in a significant savings to airline companies.Comment: 8 pages, submitted to the Journal of Air Transport Managemen

A combined optimization–simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities

The timely handling of passengers is critical to efficient airport and airline operations. The pandemic requirements mandate adapted process designs and handling procedures to maintain and improve operational performance. Passenger activities in the confined aircraft cabin must be evaluated to potential virus transmission, and boarding procedures should be designed to minimize the negative impact on passengers and operations. In our approach, we generate an optimized seat allocation that considers passengers’ physical activities when they store their hand luggage items in the overhead compartment. We proposed a mixed-integer programming formulation including the concept of shedding rates to determine and minimize the risk of virus transmission by solving the NP-hard seat assignment problem. We are improving the already efficient outside-in boarding, where passengers in the window seat board first and passengers in the aisle seat board last, taking into account COVID-19 regulations and the limited capacity of overhead compartments. To demonstrate and evaluate the improvements achieved in aircraft boarding, a stochastic agent-based model is used in which three operational scenarios with seat occupancy of 50%, 66%, and 80% are implemented. With our optimization approach, the average boarding time and the transmission risk are significantly reduced already for the general case, i.e., when no specific boarding order is specified (random boarding). If the already efficient outside-in boarding is used as a reference, the boarding time can be reduced by more than 30% by applying our approach, while keeping the transmission risk at the lowest level

A combined optimization-simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities

The timely handling of passengers is critical to efficient airport and airline operations. The pandemic requirements mandate adapted process designs and handling procedures to maintain and improve operational performance. Passenger activities in the confined aircraft cabin must be evaluated to potential virus transmission, and boarding procedures should be designed to minimize the negative impact on passengers and operations. In our approach, we generate an optimized seat allocation that considers passengers' physical activities when they store their hand luggage items in the overhead compartment. We proposed a mixed-integer programming formulation including the concept of shedding rates to determine and minimize the risk of virus transmission by solving the NP-hard seat assignment problem. We are improving the already efficient outside-in boarding, where passengers in the window seat board first and passengers in the aisle seat board last, taking into account COVID-19 regulations and the limited capacity of overhead compartments. To demonstrate and evaluate the improvements achieved in aircraft boarding, a stochastic agent-based model is used in which three operational scenarios with seat occupancy of 50\%, 66\%, and 80\% are implemented. With our optimization approach, the average boarding time and the transmission risk are significantly reduced already for the general case, i.e., when no specific boarding order is specified (random boarding). If the already efficient outside-in boarding is used as a reference, the boarding time can be reduced by more than 30\% by applying our approach, while keeping the transmission risk at the lowest level

A Comparison of Algorithms That Estimate the Effectiveness of Commercial Airline Boarding Strategies

The number of passengers carried by commercial aircraft has increased dramatically over the past 50 years, closely in-step with advances in aircraft design. This makes unloading and loading an aircraft, called turn-around time, critical to the success of the airport, the aircraft and the airlines. A number of mathematical algorithms have been developed over the years that purport to determine the most efficient boarding strategy for passengers by decreasing turn time. This thesis evaluated the boarding strategies most often used by the airlines and algorithms used to predict boarding efficiency. The models used were obtained from the literature and from personal communication with the authors. The strategy and the model associated with the greatest predicted reduction in turn-around time, and the amount of time to deplane and enplane commercial airliners was determined. The Kruskal-Wallis one way analysis of variance test was used to determine that the Random boarding strategy had the greatest boarding rate and the rotating zone strategy had the slowest. It was also determined that one of the models, the Ferarri and Nagel sensitivity analysis algorithm, was consistently predictive of the empirical observations of boarding strategies

Analytical approach to solve the problem of aircraft passenger boarding during the coronavirus pandemic

The corona pandemic significantly changes the processes of aircraft and passenger handling at the airport. In our contribution, we focus on the time-critical process of aircraft boarding, where regulations regarding physical distances between passengers will significantly increase boarding time. The passenger behaviour is implemented in a field-validated stochastic cellular automata model, which is extended by a module to evaluate the transmission risk. We propose an improved boarding process by considering that most of the passengers travel together and should be boarded and seated as a group. The NP-hard seat allocation of groups with minimized individual interactions between groups is solved with a genetic algorithm. Then, the improved seat allocation is used to derive an associated boarding sequence aiming at both short boarding times and a low risk of virus transmission. Our results show that the consideration of groups will significantly contribute to a faster boarding (reduction of time by about 60%) and less transmission risk (reduced by 85%) compared to the standard random boarding procedures applied in the pandemic scenario

A fast airplane boarding strategy using online seat assignment based on passenger classification

The minimization of the turnaround time, the duration which an aircraft must remain parked at the gate, is an important goal of airlines to increase their profitability. This work introduces a procedure to minimize of the turnaround time by speeding up the boarding time in passenger aircrafts. This is realized by allocating the seat numbers adaptively to passengers when they pass the boarding gate and not before. Using optical sensors, an agility measure is assigned to each person and also a measure to characterize the size of her/his hand-luggage. Based on these two values per passenger and taking into account additional constraints, like reserved seats and the belonging to a group, a novel seat allocation algorithm is introduced to minimize the boarding time. Extensive simulations show that a mean reduction of the boarding time with approximately 15% is achieved compared to existing boarding strategies. The costs of introducing the proposed procedure are negligible, while the savings of reducing the turnaround time are enormous, considering that the costs generated by inactive planes on an airport are estimated to be about 30 $ per minute

Contribution to the evaluation and optimization of passengers' screening at airports

Security threats have emerged in the past decades as a more and more critical issue for Air Transportation which has been one of the main ressource for globalization of economy. Reinforced control measures based on pluridisciplinary research and new technologies have been implemented at airports as a reaction to different terrorist attacks. From the scientific perspective, the efficient screening of passengers at airports remain a challenge and the main objective of this thesis is to open new lines of research in this field by developing advanced approaches using the resources of Computer Science. First this thesis introduces the main concepts and definitions of airport security and gives an overview of the passenger terminal control systems and more specifically the screening inspection positions are identified and described. A logical model of the departure control system for passengers at an airport is proposed. This model is transcribed into a graphical view (Controlled Satisfiability Graph-CSG) which allows to test the screening system with different attack scenarios. Then a probabilistic approach for the evaluation of the control system of passenger flows at departure is developped leading to the introduction of Bayesian Colored Petri nets (BCPN). Finally an optimization approach is adopted to organize the flow of passengers at departure as best as possible given the probabilistic performance of the elements composing the control system. After the establishment of a global evaluation model based on an undifferentiated serial processing of passengers, is analyzed a two-stage control structure which highlights the interest of pre-filtering and organizing the passengers into separate groups. The conclusion of this study points out for the continuation of this theme

Decision-Making Matrix to Enable Shorter Connections

MCT is known as Minimum Connection Time. It refers to the time needed for a passenger to connect from a flight to another in a specific airport, varying according to the connection type. MCT is an essential tool for airlines. It is used as an input in constructing their network and daily, specifically by operational teams, to connect or disconnect a passenger from a flight, for example, during an IROPS scenario. However, the MCT is considered the worst scenario of the variables that composes it, which means that there are opportunities for airlines to reduce the connection time in daily operations to reduce the number of misconnections. The reduction of misconnected passengers would also provide companies\u27 savings opportunities once, according to the 400 ANAC Resolution, companies must provide for misconnected passengers hotel, accommodation, and food. This research is divided into two parts, and both aim to calculate the savings opportunities considering a Flexible Connection Time. In the first part of the research, the savings are calculated assuming the real displacement time between gates, obtaining a connection time lower or equal to the MCT. In the second part of the research, a Linear Programming Model tool was used to optimize the aircraft\u27s parking position and minimize the number of misconnections, providing additional cost savings for the airlines. MCT, conhecido como Mínimo Tempo de Conexão, refere-se ao tempo necessário para um passageiro realizar uma conexão em um aeroporto especifico, variando de acordo com o tipo de conexão. O MCT é uma importante ferramenta para a companhia aérea estruturar sua malha aérea e além disso, no dia-a-dia da operação, tomar a decisão em relação a conexão e a desconexão de passageiros de um voo, durante operações irregulares. Porém o MCT é calculado considerando somente o pior cenário de todas as variáveis que o compõe, fazendo com que haja oportunidades para a empresa aérea de diminuir o tempo de conexão e com isso diminuir o número de desconectados. Essa redução pode fazer com que a companhia aérea evite gastos de acordo com a Resolução 400 da ANAC, essa que indica que o passageiro deve receber hospedagem, transporte e alimentação em caso de um atraso. Essa pesquisa é dividida em duas partes e ambas procuram calcular as oportunidades de ganho em relação a flexibilização aos tempos de conexão. Na primeira parte, o ganho é calculado considerando o tempo real de deslocamento entre os portões, obtendo a quantidade de passageiros que poderiam ter conectado em um tempo menor que o MCT. Na segunda parte, uma ferramenta de Programação Linear foi utilizada no intuito de otimizar a posição de parada das aeronaves, na busca de diminuir ainda mais a quantidade de desconectados, gerando um ganho ainda maior na economia da empresa aérea

Simulation-Free Runway Balancing Optimization Under Uncertainty Using Neural Network

International audienceThis paper proposes a new optimization scheme using neural network for runway balancing to minimize departure and arrival aircraft delay. While other researchers have proposed solutions to the runway balancing problem using a simulation-based technique to calculate aircraft delay, the proposed method replaces the simulation by a neural network model-based estimation using the actual operational data, thus providing the following two advantages. First, accurate estimation of aircraft delay can improve the solution of the runway balancing problem. Second, the simulation process is not required in the optimization. Although it is difficult to develop an accurate simulation model especially under uncertain environment, the neural network model can estimate the average delay without explicitly modeling uncertainty. In this paper, as a first step, the effectiveness of the proposed method is validated through simulations. First, simulations considering uncertainty are used to generate the data, which are then used to train the neural network. The neural network predicts the delay under the current traffic and only this predicted delay is used for the runway balancing optimization with simulated annealing. The simulation result shows that the result by neural network outperforms the one by the simulation-based method under uncertainty. This means that the neural network can accurately estimate the delay under uncertainty environment, and is applicable in the optimization process