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

Supply chain design considering cellular structure and alternative processing routings

Nowadays, in highly competitive global markets and constant pressure to reduce total costs, enterprises consider group technology and Supply Chain Management (SCM) accordingly and usually separately as the key elements for intra and inter facilities improvement. Simultaneous consideration of the elements of these two disciplines in an integrated design can result in higher efficiency and effectiveness. A three-echelon supply chain that has several markets, production sites, and suppliers is designed again in this paper as a Cellular Manufacturing System (CMS). Every product can be manufactured in the CMS through alternative process routings, in which machines are likely to fail. A linear integer programming model is presented here that seeks to minimize the intercellular movement, procurement, production, and machine breakdown costs. We present a number of illustrative examples to demonstrate the effectiveness of the integrated design. The proposed examples reveal that although the procurement and logistics costs increase slightly in the integrated design, the total cost is dropped considerably

Optimized aircraft disembarkation considering COVID-19 regulations

Passenger disembarkation takes place in the confined space of the aircraft cabin. Boarding can be regulated to a certain extent, but this does not apply to disembarking at the end of a flight. COVID-19 constraints require that cabin procedures not only be operationally efficient but also effectively reduce the risk of virus transmission to passengers. We have developed a new mathematical model that accounts for these conflicting goals. We used an already improved seat assignment for passenger groups (e.g., families or couples) and implemented a genetic algorithm that generates improved disembarkation sequences. Our use cases show a significant 40% reduction in disembarkation time when the physical spacing between passenger groups is required to comply with pandemic regulations. To inform passenger groups about the disembarkation sequence, we propose to activate the cabin lights at the seats in a dedicated way. Thus, our developed methodology could already be applied to actual airline operations

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

New mathematical model for extended arrival management capabilities

The Extended arrival management (E-AMAN) concept is based on starting the arrival traffic sequencing earlier than is the case by the arrival management (AMAN). The E-AMAN extends the horizon at which to start sequencing from the airport terminal area further upstream, to enable more smooth traffic management through speeding up, or slowing down arriving flights. Current application of E-AMAN at Heathrow, with the horizon at 350NM reduces delay, operational costs, CO2 emission and smooths delivery of arrival traffic to the runways. Here we propose an E-AMAN model that extends to 500NM. More specifically, the model incorporates three horizons: Tactical Horizon (100NM), Command Horizon (500NM), and Data Horizon (600NM). When a flight enters Data Horizon, the flight intentions are sent to the E-AMAN. When a flight enters Command Horizon, the optimizer is run to find optimal slot for that flight at the runway. Compared to previous optimisation processes, the E-AMAN takes into account the cost of delay and fuel for the airline instead of delay alone, and uses information on the distribution time of arrival to manage uncertainty (e.g. due to wind). Based on the optimal slot assigned, the E-AMAN issues a command to the flight, that can be to maintain initial speed, to speed up, or to slow down. It also assigns minutes of holding if delay cannot be absorbed during cruise. We will present some evidence of the efficiency of the optimisation process, in particular compared to a baseline scenario where the E-AMAN takes only delay into account and no uncertainty. We will show how this efficiency changes in different conditions, in particular relative to wind uncertainty

NOSTROMO - D5.2 - ATM Performance Metamodels - Final Release

This deliverable presents the third iteration of the development of the two micromodels Flitan and Mercury and the results obtained with them with the active learning process, as described in the deliverables D3.X. In this iteration, Flitan implemented concepts from PJ08.01 and PJ02.08, and Mercury implemented a module related to PJ07.02. Mercury also developed an additional module related to PJ01.01, which description is presented in Annex only, since no results could be produced in time with it for this deliverable. The development is presented in two different chapters for each simulator, with general descriptions referred to from D5.1. The modules related to each SESAR solution are described separately. The latest version of the meta-modelling process is described briefly, followed by the results obtained with the two simulators, in distinct sections. This chapter shows the performance of the meta-model with respect to approximating micro simulators

Pandemiegerechte Passagierprozesse in der Flugzeugkabine

In der Flugzeugkabine müssen sich die Passagiere während des Einsteigens, des Flugs und des Aussteigens eine enge Umgebung mit anderen Passagieren teilen, was ein Risiko für die Übertragung von Viren darstellt und angemessene Strategien zur Risikominderung erfordert. Abstände zwischen den Passagieren während des Einund Aussteigens verringern das Übertragungsrisiko, und eine optimierte Reihenfolge von Passagieren und Passagiergruppen trägt dazu bei, die Prozesszeiten erheblich zu verkürzen. Die Betrachtung von zusammen reisenden Gruppen ist dabei ein wichtiger Einflussfaktor. Die Grundidee unseres Konzeptes ist, dass die Mitglieder einer Reisegruppe nicht getrennt werden sollten, da diese bereits vor dem Betreten des Flugzeugs in engem Kontakt standen. Um jedoch den COVID-19-Vorschriften zu genügen, sollten die verschiedenen Passagiergruppen weiterhin räumlich getrennt werden. Der Ausstiegsvorgang stellt hierbei eine besondere Herausforderung dar, da die Passagiergruppen direkt informiert werden müssen, wann sie aussteigen dürfen. Bereits heute könnte die Kabinenbeleuchtung für diesen Informationsprozess genutzt werden, aber in einer zukünftigen, digital vernetzten Kabine könnten die Passagiere direkt über ihre mobilen Geräte informiert werden. Diese Geräte könnten auch dazu verwendet werden, die erforderlichen Abstände zwischen den Passagieren zu überprüfen. In der entwickelten Simulationsumgebung können wir zeigen, dass die Umsetzung einer optimierten Gruppensequenzierung unter COVID-19-Randbedingungen das Potenzial hat, die Prozesszeiten um bis zu 59% und das Übertragungsrisiko um bis zu 85% zu verkürzen

Incorporating dynamic cellular manufacturing into strategic supply chain design

For increasing the efficiency of the supply chain (SC), it is necessary to take into account the interactions and relationships between the stages of procurement of raw materials, manufacturing the products, and distributing them. An integrated framework is proposed in this paper for companies interested in meeting the demand for different products in the customer zones by establishing a number of plants and distributors at the candidate sites and in having SC design with reconfiguration capability based on changes in demand and more proper economic opportunities. For this purpose, a geographically distributed cell design is proposed for the selection of the proper location for each of the facilities and the production process of the products. A mixed integer linear programming model is presented here for the integration of the sectors for procurement, production, and distribution of the products in the SC. In light of the NP-hard class of the cell formation problem, a new algorithm titled hybrid genetic ant lion optimization (HGALO) algorithm is presented for finding the optimal or near-optimal solutions. A comparison is also made here between the proposed algorithm and the genetic algorithm (GA) for demonstration of the efficiency of the proposed algorithm. The quality of the solutions generated based on the HGALO algorithm demonstrates the capability and effectiveness of the algorithm in finding high quality solutions

COVID-19: Passenger Boarding and Disembarkation

Boarding and disembarking an aircraft is a time-critical airport ground handling process. Operations in the confined aircraft cabin must also reduce the potential risk of virus transmission to passengers under current COVID- 19 boundary conditions. Passenger boarding will generally be regulated by establishing passenger sequences to reduce the influence of negative interactions between passengers (e.g., congestion in the aisle). This regulation cannot be implemented to the same extent when disembarking at the end of a flight. In our approach, we generate an optimized seat allocation that takes into account both the distance constraints of COVID-19 regulations and groups of passengers travelling together (e.g., families or couples). This seat allocation minimizes the potential transmission risk, while at the same time we calculate improved entry sequences for passenger groups (fast boarding). We show in our simulation environment that boarding and disembarkation times can be significantly reduced even if a physical distance between passenger groups is required. To implement our proposed sequences during real disembarkation, we propose an active information system that incorporates the aircraft cabin lighting system. Thus, the lights above each group member could be turned on when that passenger group is requested to disembark

A new linear programming approach and genetic algorithm for solving airline boarding problem

The airline industry is under intense competition to simultaneously increase efficiency and satisfaction for passengers and profitability and internal system benefit for itself. The boarding process is one way to achieve these objectives as it tends to adaptive changes. To increase the flying time of a plane, commercial airlines try to minimize the boarding time, which is one of the most lengthy parts of a plane’s turn time. To reduce boarding time, it is thus necessary to minimize the number of interferences between passengers by controlling the order in which they get onto the plane through a boarding policy. Here, we determine the passenger boarding problem and examine the different kinds of passenger boarding strategies and boarding interferences in a single-aisle aircraft. We offer a new integer linear programming approach to reduce passenger boarding time. A genetic algorithm is used to solve this problem. Numerical results show the effectiveness of the proposed algorithm