DYNAMIC STAND ASSIGNMENT TO IMPROVE AIRPORT GATES OCCUPANCY

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1 DYNAMIC STAND ASSIGNMENT TO IMPROVE AIRPORT GATES OCCUPANCY Miquel Angel Piera (a), Mercedes Narciso (b), Juan Jose Ramos (c),toni Laserna (d) Autonomous University of Barcelona, Department of Telecommunications and Systems Engineering. Barcelona, Spain. (a) (b) (c) (d) ABSTRACT The Gate Assignment Problem (GAP) is an easilyunderstood but difficult to solve problem when trying to optimize a certain cost function such as the distance a passenger is required to walk inside the terminal to reach his or her departure gate. The assignment of aircraft arriving out of schedule to available gates is a major issue easy to solve when stands are placed in the same module but very difficult to solve when stands are placed in different modules due to passenger movement through the terminal. To preserve quality factor services to passengers while protecting turn-around aircraft times, most airports have modified the infrastructure by increasing the number of contact points. In this paper a challenging approach to evaluate the minimum number of stands to cope with a time window perturbation for a certain arrival traffic is presented. Emergent dynamics are analyzed when the number of gates is increased and a model to evaluate different gate assignment policies to mitigate undesirable consequences is introduced. noticed that in order to avoid idleness in handling resources, handling operations usually are scheduled to saturate workers and resources while providing a timely service. In this context, a similar event propagation appears due to a delay in the start of the fuel truck positioning operation which can cause a delay in the freedom of the truck, which can generate a delay in attending to the next fueling operation. Figure 1 illustrates some of the resources that should be properly coordinated in a turn-around operation. Keywords: Timed Coloured Petri Nets, State Space, Airports, Gate Assignment Problem. 1. INTRODUCTION The growth in air travel is outstripping the capacity of the airport and air traffic control (ATC) system, resulting in increasing congestion and delays. However, a misunderstanding of the poor utilization of the available infrastructure usually leads to greater investments in additional gates, runways and extensive pavements for taxiways and aprons. In order to avoid this expensive approach, it is important to remove nonproductive operations due to poor scheduling approaches. Different kind of perturbations such as: weather, traffic congestion in some air traffic sectors, and late departures at the origin airport, leads to a typical time window predictibility around [ ] minutes with respect to the expected arrival time for more than 40% of the landing operation along the day. Unexpected changes in the flight schedules may disrupt the initial aircraft-gate assignment, and result in congestions and delays in getting aircraft onto gates. Thus a small delay caused by a reassignment to a remote point will introduce an extra deadline on the disembarking process which will be propagated through passenger actions (ie. transfer operations) and also on the boarding process. A single delay in a certain operation can be easily propagated through all the airport subsystems. It can be Figure 1: Resources to be Schedulled in a Turn-around Operation Figure 2 shows the optimal sequence of activities that should be coordinated during the turn-around operation. As it can be easy observed, a small delay in the start of a certain operation can be easily propagated to other sequential operations to be performed in the same aircraft but also to the starting time of the operation in other aircrafts when handling systems are running short of resources to optimize costs. Figure 2: Turn-around Gantt Diagram Vol II - ISBN

2 An important source of perturbations that affects drastically handling resources availability and handling operational costs (and sometimes turns around times) is last-moment gate reassignments. In fact, each time a flight is reassigned to a remote point, it generates an extra workload to its handling company (buses and stairs together with other resources are required). To minimize last minute gate reassignments to remote points, some airports have increased the number of terminals and contact points to tackle poor arrival predictability. To tackle uncertainty by increasing the number of resources deals with a considerable economic investment, but deals also with undesirable emergent dynamics caused mainly by longer distances between terminals. Figure 3 shows the PMI airport layout in which it is easy to check that longer distance between gates forces lower handling resources productivity due to displacements. In this context, it is important to view the operations from the airport side perspective. For the airport, the flight has three phases: an Inbound Phase, a Ground Phase and an Outbound Phase. A delayed inbound flight has an impact on the Ground Phase, but also on the Outbound Phase of the flight with the same air-frame, on the crew and on the flights carrying connecting passengers. Figure 3: PMI Airport To avoid delay propagation, a deep knowledge about all the events that take place and their interactions in each phase is important. Thus, by considering the Ground Phase, the turn-around, landing, take-off and taxiing operations can be formalized as a set of interrelated events which, properly coordinated, will satisfy the aircraft operative needs under certain service quality factors (SQF). With a proper model specification considering its interactions with Inbound and Outbound phases, it will be possible to optimize operation efficiency through the proper management of airport re-sources (e.g. airport slots, stands and gates, check-in desks and baggage belts), considering the dynamics and costs of the passenger and aircraft operations. In the particular case of turn-around operations, it is easy to understand the system dynamics from a discrete event system approach, in which each operation has a certain number of preconditions, duration time estimation, and a set of post-conditions (changes in the state of airport information). A Colored Petri Net model describing the gate assignment problem has been developed to improve gate occupancy from a logistic point of view. 2. SYSTEM DESCRIPTION There are several research works that can be found in the literature (Cheng 1998; Dinga 2005; Haghani and Chen 1998; Hon 1999; Yan 2002) optimizing gate assignments according to several QoS passenger parameters, such as minimizing the walking distance between terminals for transfer operations. Most of the proposed algorithms focus either on the strategic aspects or in the operational aspects. Airport infrastructure over sizing is often a consequence of underestimating the influence of operational issues at the design stage. The gate over-sizing approach usually deals with hard to solve drawbacks in both areas: Strategic: o The increment of contact points, usually leads to more complex airport layouts. o Due to an unnecessary increment of gates, longer distances must be properly managed by employees and passengers. Operational: o Airlines Constraints: Some airlines prefer to allocate their flight in the same area to reduce passenger handling costs. o Passenger Constraints: Transfer connectivity is highly dependent of the distances between terminals when flights are late on the arrival. o Handling Constraints: Most handling companies prefer to concentrate in a narrow area all the operations to be served in the same time horizon to the different flights. o ATC Constraints: Complex layouts increase the number of taxiways to be properly managed to support a safety movement of aircrafts. Thus, strategic design decisions affect the performance of most airport operations: handling, taxiing, transfers, etc. Furthermore, gate occupancy usually is quite low regarding the number of aircrafts that are assigned to remote points due to discordances between the ETA (Estimated time of Arrival) and the arrival time, and also between the departure time and the ETD (Estimated time of Departure). A proper analysis of the cause-effect relationship between the main perturbation factor that decreases the contact point occupancy and increases the remote point occupancy should contribute to improve quantitatively and qualitatively the airport operations and consequently reduce the cost of airport taxes, handling and airlines. Vol II - ISBN

3 Figure 4 illustrate the result of a typical gate assignment process in which ETA s and ETD s are used in the gate occupancy optimization. T 4 T 4 T 5 T 6 T 8 T 9 JKR 400 T 7 T 10 Gate 7 Gate 6 Gate 5 Gate 3 Gate 2 Gate 1 Remote T 4 T 5 T 6 T 8 T 9 T 11 T 12 JKR 4310 JKR 400 AEA 350 T 3 T 7 T 10 Figure 6: Late Arrival Perturbation Legend: Turn around time Gate maintenance Gap Figure 4: Gate Planning Despite the assignment proposed to gates nº 1 and nº 3 should provide a better gate occupancy, at the end of the day, the occupancy of both usually is lower than gates with an initial poor occupancy assignment such as gates nº 6 and nº 7. Figure 5 illustrates the time events that affect the state of gate nº 4 according to the planned flights arrival and departures. 2. Early Arrival: Due to poor predictability, a considerable amount of aircraft gets the destination airport earlier than the ETA. An earlier arrival could overlap two aircraft on the same gate. Figure 7 (top part) illustrates an earlier arrival perturbation of aircraft AEA310 (with ETA in T 4 and landing at T 4,, T 4 >T 4 ), together with the result of the rescheduling (bottom part). T 4 T 5 JKR 4310 T 4 T 5 T 6 T 8 T 9 T 11 T JKR JKR AEA T 3 T 7 T 10 Remote T 4 T 5 T 6 JKR 4310 T 8 T 9 T 11 T 12 JKR 400 AEA 350 T 3 T 7 T 10 Figure 5: Estimated Events on Gate nº 4 Analyzing the main causes of poor gate occupancy of an optimal gate assignment, there are 3 different causes that affects drastically to contact point occupancies: 1. Late Arrival: Due to constant ATFM perturbations, a considerable amount of flights have a delay on the arrival around 15 minutes. When gate assignments have been planned according to a high occupancy value, a late arrival could overlap two aircrafts in the same gate for a short period of time. To avoid this situation, usually late flights are re-scheduled to remote points. Figure 6 (top part) illustrates a late arrival perturbation of aircraft AEA310 (with ETA in T 4 and landing at T 4, T 4 <T 4 ) together with the result of the rescheduling (bottom part). Figure 7: Earlier Arrival Perturbation 3. Late Departure: Finally, due to last moment problems such as technical problems with the aircrafts, late clearance due to taxi-out saturation, or a delay in the embarkement, sometimes the gate is not released at the ETD and the next flight can be re-scheduled to a remote point. Figure 8 (top part) illustrates a departure delay perturbation of aircraft AEA310 (with ETD in T 5 and departure at T 5, T 5 < T 5 ), together with the result of the re-scheduling (bottom part). Vol II - ISBN

4 Remote T 4 T 5 T 6 JKR 4310 T 3 JKR 400 T 7 T 8 T 9 T 4 T 5 T 7 T 8 T 9 JKR 400 T 6 AEA 350 T 10 Figure 8: Late Departure Perturbation T 11 T 12 As it can be seen in the bottom part of figures 7,8 and 9, there is a high correlation between ETA and ETD perturbations and a low gate occupancy factor when the original planning strategy tries to maximize the gate occupancy factor. 3. COLORED PETRI NET FORMALISM Colored Petri Net formalism are well known by the OR and Simulation community for their capability in simulating and analyzing discrete event system (Jenssen 1997). In this section, the CPN model developed to determine the consequences of a ETA and ETD perturbations on different gate configurations is illustrated. CPN has been chosen as the modeling formalism due to its ability to describe the complete structure of a system together with its behavior and the information about the system state (Piera, Narciso, Guasch and Riera 2004) through the use of a functional programming language. A Petri Net can be defined as a bipartite directed graph describing the structure of a discrete event system, while the dynamics of the system is described by the execution of the PN. A PN is coloured if the tokens are distinguishable. The main CPN components are: state vectors, arc expressions and guards, color sets, places and transitions. See (Jensen 1997; Piera, Narciso, Guasch and Riera 2004) for the description of these terms and tutorial on CPN. Mathematically, a CPN can be defined as a tuple of 9 elements, CPN = (, P, T, A, N, C, G, E, I). The meaning of each tuple is described below: = on nc indicates the color definition: Finite set and not empty. This allows the specification of attributes that must be defined for every entity type that needs to be modeled. is the color (attribute) of the entity type i. P = Indicates place nodes: Finite set of place nodes that permits the system s state specification. It contains the number of tokens and the colors of each token. T = Represents transitions: Finite set of transition nodes. A = Represents the arcs: Finite set of arcs. N: Node Function, allows the association of each arc with its node terminals (the origin and destination nodes). The nodes must be of different types; i.e. if a node is a transition, the connecting node must be a place and vice versa. C: Set of color functions, allows the specification of the entity type that can be stored in every place node: G: Guard function associated with transition node, enables or disables an event associated with the transition in function of the value of the entity attributes to be processed. E: Arc expression, indicates which type of tokens can be used to fire a transition. I: Initialization function, specifies color values of the initial tokens stored in each place. This is referred to as the initial state of the CPN. 4. CPN GATE ASSIGNMENT MODEL To model the gate assignment problem as a discrete event system, it is necessary to define the events that are relevant to the gate occupancy factor. CPN allows the representation of a system in a compact structure with few places and transitions. Table 1 summarizes the meaning of the events modeled Transition T1 T2 T3 T4 Table 1: Transition Definitions Meaning A new gate is generated each time an arrival is programmed and there are no free gates for the demanded slot Match a gate with a programmed arrival A new gate is generated due to perturbations on the programmed time that leads an aircraft without a gate. Releases a gate once the aircraft start the taxi-out operation. T5 It generates a ETA perturbation according to the predictability parameter. The relevant information to determine the evolution of the different states of the gates according to estimated time arrivals and estimated time departures together with delay perturbations is supported by the places described in table 2 and colours described in table 3. Table 2:Place Node Descriptions Place Colour Meaning P1 A Programmed flights. P2 C Available Airport Gates P3 A Gates pending to be released P4 A Aircrafts landed P5 D Flow Control Information Table 3: Colour Descriptions Colour Definition Meaning A Product a_id*t_ar*t_r Aircraft Vol II - ISBN

5 Information A_id integer Aircraft identifier T_ar integer Estimated Time of Arrival T_r integer Round time C Product Gate Information g_id*gt_id*gt_i*gt_f*ge G_id integer Gate identifier Gt_id integer Terminal identifier gt_i Integer Initial time for gate occupancy Gt_f Integer Final time of gate occupancy ge integer State of the gate D Product ng*nfg Global information Ng Integer Number total of gates nfg integer Number of free gates at a certain time instant Figure 10: Gates required in a non preemptive policy To avoid over sizing contact point infrastructure, some terminals are designed to support passenger movements between gates when in the same terminal area it is possible to swap between gates with practical no distance increments to passengers. Figure 11 illustrates terminal configurations in which gate rescheduling is fully supported by the layout configuration. Figure 9 illustrates the gate assignment problem specified under the colored petri net formalism. The proposed model computes the number of gates required to attend the handling operations on a certain airport with aircrafts arriving with a programmed slot every 2 minutes. The model doesn t consider remote points, thus the results generated correspond to the ideal scenario in which all passenger will embark/disembark through a contact point. A predictability of ±15 minutes is considered, and all gates are allocated at the ETA and released at ETD, thus when a perturbation affects an arrival or a departure a free gate is assigned to the aircraft and the original programmed gate remains busy. Figure 11: Gate Sharing Configuration Figure 12 summarizes the results obtained under a preemptive policy for different predictability contexts (from 0 perturbations until ±15 minutes) in which gates of the same terminal can be re-scheduled without affecting passenger QoS. Figure 9: Gate Assignment Model The minimum number of gates required to attend the slot demand is 22 gates when there are no perturbations affecting the ETA s neither the ETD s. Figure 10 summarizes the results obtained under a nonpreemptive policy for different predictability contexts (from no arrival time perturbations until ±15 minutes delays). Figure 12: Gates Required in a Preemptive Policy It can be noticed that the possibility of last moment gate re-scheduling between gates placed in the same terminal (in a narrow area) can reduce considerably the Vol II - ISBN

6 infrastructure investments while preserving passenger QoS. Figure 13 shows the CPN model with the new constraints to support gates rescheduling under a preemptive policy. Hon Wai Chun et al., HKIA SAS: A Constraint- Based Airport Stand Allocation System Developed with Software Components. IAAI-99 Proceedings. Piera, M.A.; Narciso, M.; Guasch, T. and Riera, Optimization of Logistic and Manufacturing Systems through Simulation: A Colored Petri Net- Based Methodology. SIMULATION: Transactions of The Society for Modeling and Simulation International, 80, Yan S., et al.,2002. A simulation framework for evaluating airport gate assignments. Transportation Research.Part A, 36, Figure 13: Gate assignment model with a preemptive policy 5. CONCLUSION This paper provided an overview of the Stand Allocation System from an strategical and operational point of view. Right now, most European flights have predictability around ±15 minutes on arrivals and departures. The future SESAR project seeks predictability around ±1 minute in the arrivals. The results obtained show that reducing uncertainty in the arrivals, its is possible to minimize infrastructure investments. Furthermore, layout design should be very sensitive to ATFM predictability to avoid gate saturation or idleness. ACKNOWLEDGMENTS This work is partly funded by the Science and Innovation Ministry of the Spanish Government Discrete Event Simulation Platform to improve the flexible coordination of land/air side operations in the Terminal Maneuvering Area (TMA) at a commercial airport CICYT Spanish program TRA /TAIR, and the ATLANTIDA project: New technologies applied to UAV's for research and ATM development" (CEN )*. REFERENCES Cheng, Y Rule-based reactive model for the simulation of aircraft on airport gates. Knowledge-Based Systems Dinga,H The over-constrained airport gate assignment problem. Computers & Operations Research, 32, Jensen K., Colored Petri Nets: Basics concepts, analysis methods and practical use. Springer, Vol. 1, 2, 3. Haghani A., Chen M Optimizing Gate Assignments at Airport Terminals. Transportation Research.-A, 32, Vol II - ISBN