USE OF COLORED PETRI NETS TO MODEL AIRCRAFT TURNAROUND AT AN AIRPORT

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1 6 th International Conference from Scientific Computing to Computational Engineering 6 th IC-SCCE Athens, 9-12 July, 2014 IC-SCCE USE OF COLORED PETRI NETS TO MODEL AIRCRAFT TURNAROUND AT AN AIRPORT Francisco Fernández de Líger 1, Miquel A. Piera Eroles 2, and Jenaro Nosedal-Sánchez 2 1 INECO, web page: javier.moreno@ineco.com 2 Universitat Autònoma de Barcelona, Technical Innovation Cluster in Aeronautical Management Dept. of Telecommunication and Systems Engineering. web page: {miquelangel.piera, jenaro.nosedal}@uab.cat Keywords: Simulation, Resource management, Scheduling, Colored Petri Nets, Turnaround, Airport. Abstract. The fierce competition between air transport carriers to provide a good quality services at reduced prices, forces them to improve at operational level their decision making process in such a way that the scheduled land-side and air-side operations could be preserved in presence of disturbances without introducing non-added-value operations such as buffering or redundant resources. An important source of perturbations that drastically affects the airline KPI s use to emerge at the airports, since there is a tight coupling between different operations performed by different stakeholders that should be performed at the right time and at the right place in an environment in which an event can block/freeze/delay a certain task of a particular aircraft which indirectly can affect the rest of the aircraft operating at the airport. In this paper we illustrate by means of a causal modeling methodology the analysis of the interdependencies between the processes that co-exist during the aircraft turnaround in order to predict the impact on the turnaround as a whole. The proposed modeling approach is suitable for the design of mitigation control mechanism to avoid the free propagation of the disturbances through the turnaround, enhancing the airport capacity to mitigate network perturbations. 1 INTRODUCTION The turnaround of an aircraft is defined as the set of process that coexists to prepare an inbound aircraft for a following outbound flight that is scheduled for the same aircraft, and that are scheduled during the time interval between the on-block and off-block time of the aircraft at a parking position. Some of the activities that should be properly coordinated are the inbound and outbound exchange of passengers, crew, catering services, cargo and baggage handling, together with technical activities such as fuelling, routine engineering checks and cabin cleaning. According to [11,12] most of these activities should be considered with a stochastic behavior which can impact drastically not only on the airline [1,3,4] and the airport KPI s but also its effects can propagate to the full ATM system [2,5,7]. Note for example that a small delay in the initialization of a certain turnaround process can block the initialization of post-sequence activities due to precedence constraints leading sometimes to an accumulated delay above 15 minutes which forces to re-schedule a new departure slot affecting in this way the full ATM system. To avoid delay propagation, a deep knowledge about all the events that take place and their interactions during the turnaround is important not only to predict the effects of small deviations on the initialization tasks, but also to design mitigation mechanism that provide some control on the propagation of delays between the turnaround tasks. With a proper model specification that takes into account the different interdependencies and its dynamics between the turnaround tasks it would be possible to optimize operation efficiency through the proper management of handling/airline/airport resources considering the dynamics and costs of the passenger services and aircraft operations. In order to deal with a proper analysis of the Turnaround Process, it should be considered at two modeling levels: Macro level description: The turnaround can be considered as a compound of several processes that run in parallel and converge to the aircraft. These sub-processes are: Passenger Process: all activities around the passenger that allow him/her to board the plane and fly

2 or to leave the plane and reach to its destination. Baggage Process: All activities around the passengers baggage that allow the baggage to travel at the same time as its owner and be delivered to him/her at the destination airport. Cargo/Freight Process: all activities around the cargo transported in the aircraft bellies that allow it to be flown from its origin to its destination. Ramp Process: all activities around an aircraft at an airport that prepare it for the next flight. It encompasses activities such as passengers boarding and deplaning, Baggage unloading and loading, Catering, Re-fuelling, Water Services. It is the Core Process of the turnaround and the rest of the abovementioned ones are inputs to it. Micro Level Description: The different interdependencies between the activities should be properly formalized in order to be able to trace the mechanisms that foster the free propagation between the sub-processes. These interdependencies should be described at: Time domain: Time precedence relationship constraints the earliest time at which an activity can be scheduled. Thus for example, the boarding process cannot be started before the deplaning. Spatial Domain: Since all the activities should confluence in the aircraft, space around the aircraft has become a shared resource that can easily block or delay operations, transforming cooperative tasks into competitive tasks trying to catch the space required to perform the operation. Figure 1 illustrates the importance of a proper coordination of the use of the different areas around the aircraft when executing a turnaround operation. Actors Domain: Some activities are performed by different operators while some others are executed by the same operator, depending on the business model implemented. The assignation of several task to the same operator do not always means an increment of the turnaround time neither a decrement on the cost, but assigning more than one activity to the same operator could be a source of delays in case of perturbations. Resources Domain: There are several activities such as fuelling that requires particular equipment while there are other activities such as loading / unloading that share the same equipment. It is worthwhile to note that resource sharing is not performed only at turnaround level, but also at airport level since some resources are shared between the turnaround processes that take place in parallel between different aircraft. Regulation Domain: Safety is an important requirement in most aeronautical operations. Thus, in the turnaround process, there are also some tasks which execution depends on some regulation conditions, such as for example, to preserve an exit pathway of the fuel tank during the re-fuelling operation. Figure 1. Required space for the turnaround process

3 A proper specification of the turnaround process at micro level will provide the right tool: To evaluate the impact at macro level of any time deviation of the tasks in order to coordinate a corrective action between the different stakeholders that could avoid the propagation of delays to other land-side or even air-side processes. To optimize the turnaround process by removing non-added-value operations which at present emerge due to conservative policies in the 5 interdependency domains already described. In this paper, it is presented how perturbations affect the turnaround process by means of a Colored Petri Net (CPN) model that formalizes the different interdependencies between the turnaround tasks. The objectives are to consider the impact of different changes in the task interdependencies into the whole turnaround process. The impact analysis is based on the quantitative effects that produce a late initialization of a task, a late finalization of a task or a change in the interdependencies. The remainder of this paper is organized as follows. Section 2 introduces the Colored Petri Net formalism while in Section 3 the CPN model developed is illustrated. Section 4 summarizes the inefficiencies of delays in the overall turnaround time, and illustrates the impact of several combinations of delays in the turnaround time. Section 5 describes the conclusions on the importance of using causal models to analyze turnaround task s interdependencies. 2 TURNAROUND TASK FORMALIZATION Traditional modeling of the turnaround sub-processes has been made based on linear models such as the one represented in figure 2 which illustrates the Baggage Loading Process. This classical approach provides a descriptive model that allows for the characterization of the different sub-processes and the actors involved in each of them. Quantification of time required by each process may also be introduced. Cockpit Crew Inform load figures and confirm loading Instructions Provide a signed copy of Loadsheet Baggage Handling Operator Drive dollies with loaded containers/pallets to stand Load standard ULD s into the Aircraft Load priority ULD s into the Aircraft Load Cargo Last minute baggage? Take last minute baggage to the stand and load bulk Load figures no Pallet Special baggage? Load Hold 5 Retry container/ pallet loaders Remove Baggage Dollies and Lift Loaders from stand no Bulk or Palletized Baggage? Missing Passenger? Search and remove Baggage Transport baggage to terminal Bulk Close Hold Doors Drive baggage carts with bulk to stand Load bulk baggage Load bulk priority baggage Load Bulk Cargo Last minute baggage? Take last minute baggage to the stand and load bulk no Special baggage? Load Hold 5 Retry conveyor belt from main holds Remove Baggage Carts and conveyor belt from stand no Search and Missing Transport baggage remove Passenger? to terminal Baggage Figure 2. Linear description of the Baggage Loading turnaround sub-process However, the complexity of the process cannot be analyzed easily by a flow oriented description of a process as a sequence of well organized tasks with an initial and ending time. Note for example, that the number of actors (i.e. Airport Operator, Air Navigation Service Provider, Airline, Security Forces, Passenger Services Agent, (Ramp) Handling Agent, Cargo Agent, Fuelling Supplier, and Catering Supplier among others) that

4 intervene during the turnaround, most times belonging to different organizations and with limited information sharing among them influence drastically in the complexity of the turnaround process and cannot be easily analyzed from the linear model description. Each actor has a role and responsibility in different parts of the different turnaround sub-processes. Any failure to meet its objectives of any of these actors in any of the different sub-processes may lead to a delay in the departure of the flight. As alternative to flow oriented modeling formalism [8], discrete event system formalisms (DES) provide for each event (i.e. initialization or end of a task) all the preconditions, duration time estimation, and the set of postconditions that describes completely the state of the system. Among the DES formalisms, Colored Petri Nets [6] have proved to be successful tools for modeling complex systems due to several advantages such as the conciseness of embodying both the static structure and the dynamics, the availability of the mathematical analysis techniques, and its graphical nature [9,10]. The main CPN components that fulfill the modeling requirements are: Places: They are very useful to specify both queues and logical conditions. Graphically represented by circles. Transitions: They represent the events of the system. Graphically represented by rectangles. Input Arc Expressions and Guards: Are used to indicate which type of tokens can be used to fire a transition. Output Arc Expressions: Are used to indicate the system state change that appears as a result of firing a transition. Color Sets: Determines the types, operations and functions that can be used by the elements of the CPN model. Token colors can be seen as entity attributes of commercial simulation software packages State Vector: The smallest information needed to predict the events that can appear. The state vector represents the number of tokens in each place, and the colors of each token. The Color sets will allow the modeler to specify the entity attributes. The output arc expressions will allow specifying which actions should be coded in the event routines associated with each event (transition). The input arc expressions will allow specifying the event pre-conditions. The state vector will allow the modeler to understand why an event can appears, and consequently to introduce new pre-conditions (or remove them) in the model, or change some variable or attribute values in the event routines to disable active events. The causal analysis of a CPN model can be performed by means of the reachability tree [8] which allows to determine: All the events that could appear according to a particular system state: Given a particular estate in the turnaround process, the reachability tree can informs about the next activities that could immediately start and/or finalize, and also the perturbations that could appear and its effects in the turnaround. All the events that can set off the firing of a particular event: By analyzing the undesirable states (those provoked sometimes by the presence of perturbations), it is possible to determine those events which leads to those states but also the events that prevents the system to reach an undesirable state (i.e. Poor performance of the turnaround process). All the system states (markings) that can be reached starting from a certain initial system operating conditions M 0 : Given a certain amount of resources (human resources and equipments) in the parking position, the reachability tree allows the computation of all the states that can be reached by the different activities during the turnaround. The transition sequence to be fired to drive the system from a certain initial state to a desired end-state. Since the initial conditions of a particular turnaround are well known, it is possible to use the reachability tree to determine the best sequence of activities that minimizes a certain cost function (i.e. The overall turnaround time or its costs).

5 3 CPN MODEL OF THE TURNAROUND PROCESS The model implemented considers the activities described in the Airbus AIRCRAFT CHARACTERISTICS AIRPORT AND MAINTENANCE PLANNING Manual. Each operation is numbered in the first column, the tasks precedence s are described in column nº 3 and the proposed duration is described in column 4.The last column summarizes the average delay analyzed using field data. Task id Task description Precedences Duration (minutes) Delays (minutes) 1 Placing the PBB 2 * 2 Deboarding at L Boarding at L1 2_ Headcounting 3 2 * 5 Moving out the PBB 4 2 * 6 Placing the catering vehicle at R1 2 * 7 Catering at R1 2_ Moving out the catering vehicle at R1 7 2 * 9 Driving cat vehicle to R2 8 1 * 10 Placing the catering vehicle at R2 9 2 * 11 Catering at R Moving out the catering vehicle at R * 13 Placing cleaning vehicle 2 * 14 Cleaning 2_ Moving out the cleaning vehicle 14 2 * 16 Placing lower deck cargo loader front 1 * 17 Unload lower deck cargo front Load lower deck cargo front 11_ Moving out lower deck cargo loader front 18 1 * 20 Placing lower deck cargo loader rear 1 * 21 Unload lower deck cargo rear Load lower deck cargo rear 11_ Moving out lower deck cargo loader rear 22 1 * 24 Placing conveyor belt 1 * 25 Bulk unload Bulk load 11_ Moving out conveyor belt 26 1 * 28 Placing fuel hydrant dispenser or tanker 2 * 29 Re-fuelling 2_ Moving out fuel hydrant dispenser or tanker 29 2 * 31 Placing Potable water vehicle 2 * 32 Potable water servicing 31 4 * 33 Moving out potable water vehicle 32 1 * 34 Placing lavatory vehicle 33 2 * 35 Toilet servicing 34 5 * 36 Moving out lavatory vehicle 35 1 * Table 1. Turnaround operations for A-320 Figure 3 illustrates the CPN model with the precedence interdependencies, in which transition Ini Task and End Task are used to schedule the different tasks which precedence pre-conditions has been satisfied, while the rest of transitions are used to check and update the precedence pre-condition information.

6 Place with the list of tasks Transitions to schedule the task s execution Place with the task s precedences Figure 3. CPN model with precedence interdependencies formalized The analysis of the reachability tree allows to obtain the best sequence of events that minimizes a certain cost function. In Figure 4 it is represented by means of a Gantt chart the sequence of tasks together with its duration time (without considering delays) which minimizes the overall turnaround time. In red color it has been marked the critical path of the turnaround which is determined by parallel and sequential processes that limit the shortest turnaround time due to dependencies among each other. Meaning that, any delay in the execution of one of them can be extended to the consecutive ones causing the delay of the whole turnaround. At some cases, this delay can be recovered if the next activities are performed in less time. Figure 4. Gantt chart representation of the shortest turnaround generated by the CPN model

7 4 DELAY INEFFICIENCIES IN THE OVERALL TURNAROUND TIME Statistical analysis has been performed using field data to identify those delays that occurs more frequently during the turnaround. Column nº 5 of table nº 1 summarizes the delays considered in this study, while figure 5 illustrates the statistical result obtained when analyzing the boarding time. Summary for Boarding A nderson-darling Normality Test A -Squared 0,80 P -V alue 0,032 Mean 22,083 StDev 2,977 V ariance 8,862 Skew ness -1,06964 Kurtosis 0,92872 N 24 Minimum 15,000 1st Q uartile 20,250 Median 22, rd Q uartile 24,000 M aximum 26,000 95% C onfidence Interv al for Mean Mean 95% Confidence Intervals 20,826 23,340 95% C onfidence Interv al for Median 21,000 24,000 95% C onfidence Interv al for StDev 2,314 4,176 Median 21,0 21,5 22,0 22,5 23,0 23,5 24,0 Figure 5. Statistical Analysis of the boarding process The reachability tree provides the impact of the different delays described in column 5, which can be computed by the CPN model as isolated delays or also as a combination of delays, in order to identify those perturbations which requires special monitoring conditions and also to design control mechanism to avoid the free propagation of the delay to the whole turnaround process. Figure 6 illustrates a combination of delayed tasks (indicated by the double arrows in tasks 14, 17, 21, 25, 26 and 29) that as null impact in the turnaround time, a poor performance in the execution in these tasks does not change the original critical path. Figure 6. Gantt representation of the hugest combination of delayed tasks without impact to the turnaround time In contrast, there are some other tasks, that in case of delay, by it self induces a new and longer critical path (e.g. tasks 18 and 22). 5 CONCLUSIONS This paper focuses on the advantages of using causal simulation models to understand the impact of delays on the turnaround process in order to design policies and strategies that could mitigate the propagation of delays through the turnaround and avoid any impact in the airport KPI s. An exhaustive timed analysis of the different perturbations that can affect the turnaround time due to precedence task interdependencies has been used to illustrate the complexity of the turnaround process at operational level, and emergent dynamics that affect the performance of the overall turnaround operation have also been analyzed. The causal analysis of the different perturbations has been specified using the CPN formalism to design different policies and strategies for a robust and efficient turnaround that mitigates undesirable dynamics. The results obtained allow a classification of delays by its impact on the turnaround (see figure 7).

8 ACKNOWLEDGEMENTS Figure 7. Impact of delays in the turnaround process modeled in CPN This work is partially funded by INnovative TEchnologies and Researches for a new Airport Concept towards Turnaround coordination (INTERACTION project) web page: REFERENCES [1] Barnhart C and Bratu S. (2001), National Trends in Airline Flight Delays and Cancellations, and the Impact on Passengers. Proceedings of the Workshop on Airline and National Strategies for Dealing with Airport and Airspace Congestion, College Park, Maryland, USA. [2] Carlier S, De Lépinay I, Hustache J-C, et al. (2007), Environmental impact of air traffic flow management delays. Proceedings of the 7th USA/Europe air traffic management R&D seminar. Barcelona, Spain. [3] Bloem M and Huang H. (2011), Evaluating Delay Cost Functions with Airline Actions in Airspace Flow Programs. Proceedings of the Ninth USA/Europe Air Traffic Management Research and Development Seminar, Berlin, Germany. [4] Federal Aviation Administration (FAA). (2000), Air Carrier Flight Delays and Cancellations. Report Number: CR [5] Gwiggner C, Kageyama K and Nagaoka S. (2008), Propagation of airspace congestion. An exploratory correlation analysis. Proceedings of the 3rd International Conference on Research in Air Transportation (ICRAT), Fairfax, Virginia, USA. [6] Jensen K., (1997), Colored Petri Nets: Basics concepts, analysis methods and practical use. Springer, Vol. 1, 2, 3. [7] Mukherjee A, Hansen M and Grabbe S., (2012), Ground Delay Program Planning Under Uncertainty in Airport Capacity. Transport Plan Techn; 35(6): [8] Piera, M.A.; Narciso, M.; Guasch, T. and Riera, (2004). 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, [9] Piera MA., Ramos JJ and Robayna E. (2009), Airport Logistics Operations. Simulation-Based Case Studies in Logistics. London: Springer: [10] Piera MA., and Baruwa O., (2008), A Discrete Event System Model to Optimize Runway Occupancy. Proceeding of the 7th EUROCONTROL Innovative Research Workshop and Exhibition (INO 08), France. [11] Wu C-L.,(2008), Monitoring Aircraft Turnaround Operations - Framework Development, Application and Implications for Airline Operations. Transportation Planning and Technology 31(2): [12] Wu C-L., Caves RE., (2003), Flight Schedule Punctuality Control and Management: A Stochastic Approach. Transportation Planning and Technology, 26(4):