In this project, we have developed the Ramp ActivityCoordination Expert System (RACES) in order to solve aircraftparking problems. RACES includes a knowledge-basedscheduling system which assigns all daily arriving and departingflights to the gates and remote spots with domain specificknowledge and heuristics acquired from human experts.RACES processes complex scheduling problems such asdynamic inter-relations among the characteristics of remotespots/gates and aircraft with various other constraints, forexample, customs and ground handling factors at an airport.By user-driven modeling for end users and near optimalknowledge-driven scheduling acquired from human experts,RACES can produce parking schedules for about 400 dailyflights in approximately 20 seconds, whereas it normally takeshuman experts 4 to 5 hours to do the same. Scheduling results inthe form of Gantt charts produced by RACES are also acceptedby the domain experts. RACES is also designed to deal with thepartial adjustment of the schedule when unexpected events occur.After daily scheduling is completed, the messages for aircraftchanges and delay messages are reflected and updated into theschedule according to the knowledge of the domain experts. Byanalyzing the knowledge model of the domain expert, thereactive scheduling steps are effectively represented as the rules,and the scenarios of the Graphic User Interfaces (GUI) aredesigned. Since the modification of the aircraft dispositions,such as aircraft changes and cancellations of flights, are reflectedin the current schedule, the modification should be sent toRACES from the mainframe for the reactive scheduling. Theadjustments of the schedule are made semi-automatically byRACES since there are many irregularities in dealing with thepartial rescheduling.
PROBLEM DESCRIPTION
The aircraft-parking problem is a scheduling problemwhich entails assigning every arriving and departing flightto in-terminal gates and remote gates satisfying variousdemands. It is a kind of scheduling problem which alsoincludes job-shop scheduling if we consider the (remote)
gates as machines and incoming flights as jobs. Inaddition, this problem entails characteristics of temporalreasoning mechanisms. Theoretically, this problembelongs to the NP-class of problems in computationalcomplexity. That is, if we try to assign m flights to nremote parking spots or gates (often referred to as bridges),then a non-polynomial number of combinations (m!)n arepossible. The scheduling becomes more dynamic anddifficult if the frequency of arriving and departing flightsincreases and unexpected events occur during theoperational stage in a given time period. Increased airtraffic and customs requirements also reflect difficulties inproducing parking schedules. In practice, professionalschedulers manually make the schedules once a day. Thisrequires domain-specific knowledge, experience andheuristics and requires a considerable amount of time andtedious paper work to complete.
Traditionally researchers have used mathematicalprogramming techniques to solve these kinds of problems.However, it is very difficult to model constraints anddomain knowledge with only mathematical variables. Inaddition, there may be serious problems of remodelingand processing when unexpected events occur for large-scale practical problems. Recently, many researchers haveproposed using AI techniques such as constraint directedreasoning, expert systems and Constraint SatisfactionProblem(CSP) to solve these problems (Jo, Jung, andYang 1997, Fox 1987, Prosser 1993). AI techniquesprovide more flexible and expressive power thanmathematical programming in modeling a complexscheduling problem. Comparison between IntegerProgramming and AI is well described in another work(Dhar and Ranganathan. 1990). In addition, modeling thereactive scheduling with Integer Programming is beyondour ability to formulate due to the dynamic addition ofconstraints and the necessity of the partial solutions.
Many expert systems have been presented in these areas.Practical expert systems have developed in the airlineindustry. American Airlines developed GateManager toeffectively manage busy air traffic and resources(American Airlines 1993). The GATES system fromTexas State University controls gate assignment and
Ramp Activity Expert System for Scheduling andCo-ordination at an Airport
tracking in New York's JFK airport (Brazile and Swigger1988). Knowledge Engineering, a Singapore-basedcompany, has successfully developed a constraint-basedgate allocation system using ILOG for Changi airport inSingapore (Berger 1995).
daily flightschedules
ground handlingfactors
characteristics ofgates / remote
spots
Figure 1. The feasibility region in gate allocation
DESCRIPTION OF RACES
RACES assigns daily flights to the gates and remote spotswith domain specific knowledge and scheduling heuristics.Using domain filtering techniques, we can remove theinconsistency in the domain for variables and confine thesearch space. To find a user-driven optimal solution,RACES utilizes an efficient heuristic scheduling method tosatisfy constraints. RACES produces a near optimalschedule with considerations for the flight schedules,aircraft type, characteristics of remote spots, andconditions for ground handling. Aircraft have to beassigned to adequate remote spots and gates with thesatisfaction of given constraints. In addition, gates andremote spots are distinguished by size and hydrantfacilities. RACES can be viewed as a three dimensionalconstraint solver as in figure 1, and it also maps threedimensional spaces into the two dimensional spaces whichis represented in the form of a Gantt chart. RACES makesuser-dependent, near-optimal schedules satisfying thegiven constraints with domain specific knowledge andheuristics. RACES produces the Gantt chart whichrepresents the daily parking schedule a day beforehand.
In terms of constraint solving, there are three differenttypes of constraints to satisfy. The first is the strong-hardconstraint which has to be satisfied during the schedulingprocess. If this constraint is violated, then the solution isno longer valid. The second is the weak-hard constraintwhich can be violated in specific environments. Thisconstraint can be violated by interacting with users, but notby RACES. The last is called a soft-constraint which is
applied to specific flights and specific times. Duringscheduling, a soft-constraint is checked and there is anattempted to satisfy the constraint if possible. If not, thistype of constraint can be relaxed by RACES. RACES canbe divided into two different knowledge-based sysems.The first is to generate a one day schedule one daybeforehand, which is described in the next section. Thesecond is to adjust a schedule during an operational day.More technical details with examples for the managementof constraints and the representation of domain knowledgeis presented in another work (Jo, Jung, and Yang 1997).
INITIAL SCHEDULE GENERATION
After we have completed our documentation ofknowledge acquired from domain experts, thisknowledge is about 20 pages long, which does not includethe database description. Moreover, the knowledge itself istoo domain specific for the average person to understand.In this section, we describe the scheduling strategy beingdeployed in our system. In figure 2, RACES produces theGantt chart which represents the daily parking schedule aday beforehand with today’s schedule for co-ordination.
Figure 2. Generation of the initial schedule in RACES
Consistency by Domain Filtering
One scheduling procedure is responsible for bindingcontinuous time values to the discrete time variables inorder to satisfy constraints for the time restriction. To dealwith the continuous time domain, we break down thecontinuous time values into discrete time elements. Wealso classify all the available remote spots and gates withtime-keys. When the system processes scheduling, itfilters domains and removes elements violating
constraints in three steps. An example of the domainfiltering process for flights schedules is shown in Figure 3. In the first step, RACES filters domains with theknowledge and constraints of various aircraft. Second, thesystem filters domains with the knowledge and constraintsof towing. Finally, the system filters domains with theknowledge and constraints of available parking spots. As aresult of the process, RACES can prune the search spacesignificantly.
Knowledge-driven near optimal scheduling
We consider the optimal solution in terms of the user'sbenefit. An important factor is to minimize the number ofstand-by flights which are not yet assigned to thegates/spots due to conjestion at the airport. Duringscheduling, RACES also tries to allow for the least numberof towings at the airport. If the system fails to assignflights to the gates, the size of the aircraft must be takeninto consideration. If stand-by flights involve relativelylarge aircraft, it is difficut for users to assign themmanually after the automatic schedule is produced sincethe number of large spots is usally inadequate. There are two heuristic scheduling methods in RACES.One is the time-focused method using best-fit assignmentin terms of the time-span for parking. The other is theaircraft-size focused method. Each method has advantagesand disadvantages in finding user-driven optimal solutionsgiven that one method conflicts with the other method atcertain points during the scheduling process. In our work,to avoid conflicts between the time-span focused methodand the size-focused method, we have empirically foundand exploited a trade-off point between the two. A detailed
description of these heuristic scheduling methods ispresented in another work (Jo, Jung, and Yang 1997).
KNOWLEDGE-BASED REACTIVESCHEDULING
When the real operational day is reached after thescheduling has been completed, unexpected events canoccur as the environment changes. If sudden changes inschedules occur, such as the delay of an aircraft or thechange of aircraft for certain flights, then schedules mustbe adjusted. In adjusting the daily schedules, the domainspecific knowledge from domain expert is encoded intoRACES.
Expert Model in Reactive Scheduling
MessageReceiving
Agent
MessageHandling
Agent
Reactive Scheduling Agent
ExplanationAgent
GUIAgent
Expert Model
MetaAgent
Figure 4. Expert model in reactive scheduling
The meta-agent in Figure 4 has the knowledge of whichagent is activated depending on the message from the
International Flights Schedule
Flight pattern constraints
International toInternational Flights
International toDomestic Flights
Domestic toInternational Flights
Towing Constraints
arrivaltow
departuretow
Notow
spot and bridge constraints
domain filtered flights
multipletows
Figure 3. domain filtering before scheduling
message handling agent. Although the role of each agentcan not be summarized due to the page requirement,message handling agents and reactive scheduling agentsare explained in brief. The task of the message handling agent is to check sling,detect cycle and group the related messages together. Themessages for AC change come into RACES in a unit offlights which are not in an ordered form, but rather inmixed forms. Therefore, the message handling agent has torearrange them into a unit with an HL number by theirscheduled time. Then the agent can check the fallacy of asling order or an omitted sling. Generally, one aircraftmakes a sequence of flight during a given day. A sling is asequence of flights that an aircraft should make. A slingconsists of flight schedule that reflects a continuous timedomain. The task of the reactive scheduling agent is toreschedule according to messages received as theenvironment changes during the operation. This task canbe divided into two different operations: 1) Creating newstandby bars. 2) Assigning these bars. The mostimportant task of the reactive scheduling agent is to assignnew standby bars to the Gantt chart. This process requirescomplicated and delicate domain knowledge. A variety ofadjusting rules are implemented for these processes.
Interactive Graphic User Interfaces
The change of aircraft(AC) occurs in a case when anaircraft can not operate the flight that it is scheduled to rundue to a delay of operations, aircraft repair, or abreakdown. In this case, the flight will be operated byanother aircraft, and we call this situation an AC change.For an example, see Figure 5.
...
S3
S4
S5
S6
S7
...
A
B
C
b2
b1a2
c1
c2a1
A
B
C
b4
b3
a4
c3
c5
a3
HL A : a1 a2 a3 a4 a5HL B : b1 b2 b3 b4 b5HL C : c1 c2 c3 c4 c5
A
B
Cb5
a5
c4
HL A : a1 c2 c3 c4 c5HL B : b1 a2 a3 a4 a5HL C : c1 b2 b3 b4 b5
Figure 5. The steps for adjusting an initial schedule
Figure 6. The relationship diagram with other systems
In Figure 5, the capital letters stand for the HL numberwhich is the registration number for an aircraft, and thelower case letters stand for flight numbers. In the originalscheduling, aircraft A was supposed to arrive as flight a1and depart as flight a2. Similarly, aircraft B was supposedto arrive as flight b1 and depart as flight b2, andanalogously, C as c1 and c2. If we rotate aircraft A with B,B with C and C with A circularly, A will arrive as a1 anddepart as c2, analogously, B as b1 and as a2, and C as c1and as b2. As we have shown in figure 5, each flight has atime slot for an arrival and a departure. If we change theschedule of flights for some aircraft, the length of theoriginal solution bar may be changed. This means that weneed to adjust the solution of the initial schedule. Theaircraft rotation can occur not only between two aircraftbut also among three, four, or more aircraft. They alsooccur in a cycle as we can see in Figure 5. Since the fullyautomatic adjustments are not consistent from time to timeand they are quite complex, these are too difficult toautomate. The intelligent interactive GUI is utilized formanual adjustments.
THE RELATIONSHIP BETWEEN RACES ANDOTHER SYSTEMS
Figure 6 illustrates the relationship between RACES andother systems. RACES receives flight schedule data fromthe Computer Center in order to make an aircraft parkingschedule. After RACES performs the scheduling, ittransfers scheduling result into KASTCO(Korea AirTerminal Service COmpany) and SELMC(SEouLMaintenance Control department). KATSCO is responsiblefor performing the ground tasks of an aircraft, for example:hydrating, cleaning, and towing. With the scheduling resultobtained from RACES, KATSCO can prepare their tasksefficiently. Also, SELMC is responsible for maintenanceof an aircraft. They check device problems on an aircraft.They need the data from RACES to make theirmaintenance schedule. Actual towing of an aircraft needscooperation between KATSCO and SELMC. The aircraftparking schedules from RACES allow them to prepare totow an aircraft before they perform actual towing.
DEVELOPMENT HISTORY
The ramp activity coordination system for KAL(KoreanAir Lines) at Kimpo Airport in Korea was managed byhuman experts using manual scheduling until 1995. Inorder to maximize the utilization of the resources of theairport, KAL and Expert Systems Lab at Inha Universitydecided to develop the expert system with experiences andheuristics of domain experts at KAL. Three domainexperts and a system engineer from KAL and five graduate
students at Inha University participated in this projectbetween 1995 and 1997 under Prof. Geun-Sik Jo’ssupervision. It was approximately a $ 125,000 project forInha University, which excluded wages for three domainexperts and a system engineer, and expenses for an officeand other utilities, which were paid by KAL. It took 3months to make a prototype of RACES in order toconvince KAL to pilot the project. Then after about 6months, we were able to complete the initial schedulingpart of RACES. Most of the developing time was spent inreactive scheduling which took about 9 months. Foranother 4 months, networking routines needed for themainframe to integrate with RACES, which in turninteracts with other computers, were added to create a realenvironment. To evaluate the system, two end users testedthe results against real flight schedule data of the previous120 days’ data. Their test results were accepted by domainexperts so that RACES could be used for realenvironments. For the maintenance of RACES, weexplained source codes and trained a system engineer todo the maintenance job himself. At the present time, asystem engineer from KAL is responsible for maintainingRACES and he is doing well. We think that the declarativefeatures of Prolog have made it possible for only oneperson to do the maintenance job. In addition, at thedevelopmental stage, we designed and implementedmenus and submenus to prepare for the future extensionand update of the knowledge base. RACES was written in CHIP(Constraint Handling InProlog) Ver 5.0 under a Unix operation system on anHP/712 machine. To retrieve and store the information onflights, bridges and remote spots, Oracle DBMS was used,where an SQL interface from the Prolog code wasprovided in the CHIP system.
DEPLOYMENT PROCESS
We developed RACES(Ramp Activity CoordinationExpert System) in CHIP, which consists of about 50,000lines of Prolog code with about 70 GUI menus. RACESsolves the problem using methods similar to a humanexpert problem solving procedures. We represented andprocessed the domain specific knowledge and experiences.When the system processes scheduling, it can prune thesearch space using a domain filtering technique. RACESproduces a user-driven optimal schedule using trade-offscheduling heuristics. To test accuracy of the system, weimplemented RACES with the daily operational data of anactual airline company for about 120 days and the resultswere analyzed by domain experts. RACES has beenapproved by and continues to receive the approval ofdomain experts. The system described in this paper was successfullydeployed at Kimpo Airport in Korea. RACES has been
used at Kimpo airport by KAL since 1997. The controllersat the operational control center at KAL are now using thissystem for monitoring and controlling the assignment ofremote spots and bridges. To date, the ground controllersusing this system to actually tow aircraft, assign buses anddo other ground work are able to interact well with personsat the operations control center. As long as the airport isin operation, this system should run almost 24 hours a day.When the FIDS(Flight Information Display System) wasconnected to RACES, the time that the aircraft spentwaiting to park after landing was greatly reduced. RACES currently has the ability to reschedule inapproximately 70% of the cases in a real environmentsituation. Reactive scheduling is one of the most importanttopics for researchers to use the scheduling systems inpractice. However, when the system processes therescheduling by itself, we can often find that someadjustments are not adequate. Therefore, some of theadjustments should be done in co-operation with the usersby adjusting the schedule manually through GUI.Interactive GUI in RACES plays a supporting role byhelping users to make the right decision.
BENEFITS OF RACES
RACES made the work paradigm-shift from manual intoautomatic scheduling in the ramp activity management thatrequired domain specific human knowledge and heuristics .It is clear that the initial investment is returned within in ayear after deployment. There are, however, the followingbenefits which are not currently measurable in terms ofmoney:
l Time and cost involved in scheduling are drasticallyreduced.
l Real-time adjustment for unexpected events andweather conditions is provided for.
l Interactive GUI in RACES plays a supporting role byhelping users make the right decision at the right time.
l Aircraft waiting time for parking after landing isreduced.
l High-quality passenger service is provided becauseRACES gives prior information about aircraft parkingstatus, thereby, insuring the quick movement of anaircraft after landing.
l Objective verification of ramp activity management ispossible.Finally, RACES can perform the operations necessaryto maximize the utilization of ramp activity.
Acknowledgements
This project was successfully completed with the help of
Korean Air and colleagues at Inha unversity who provideddomain knowledge and financial support.
REFERENCES
American Airlines. 1993. GateManager, Precision TechnologiesInc.
Berger, Rainer. 1995. Constraint-Based Gate Allocation forAirports, ILOG Solver and Schedule. First International User’sConference proceedings, Abbaye des Vaux de Cernay, France :1-9.
Brazile, P., and Swigger, Kauthleen M. 1988. GATES : AnAirline Gate Assignment and Tracking. IEEE Expert 3 : 33 -39.
Croker, Albert E. and Dhar, Vasant. October, 1994. AKnowledge Representation for Constraint Satisfaction Problems.IEEE transaction on Knowledge and Data Engineering 5(5) :740 - 752.
Dhar, Vasant and Ranganathan, Nicky. March 1990. IntegerProgramming vs. Expert Systems: Experimental comparison.Communications of the ACM, pp323-336.
Dincbas, M.; Van Hentenryck, P.; Simonis, H.; Aggoun, A.; andGraf, T.. June, 1988. Applications of CHIP to industrial andengineering problems. In First Int. Conference on Industrial andEngineering Applications of Artificial Intelligence and ExpertSystems, Tullahoma, Tennessee.
Fox, M.S.. 1987. Constraint-Directed Search : A Case Study ofJob-Shop Scheduling. Research Notes in Artificial Intelligence,Fitman Publishing.
Frost, Daniel and Dechter, Rinal. 1994. In search of the bestconstraint satisfaction search. AAAI 94 : 301 – 306.
Jo, Geun-Sik; Jung, Jong-Jin; and Yang, Chang-Yoon. NOV,1997. Expert System for Scheduling in an Airline GateAllocation. Expert Systems with Applications 13(4) : 275-282.
Jung, Jong-Jin; Yang, Jong-Yoon; and Jo, Geun-Sik. July, 1997.RACES: Ramp Activity Coordination Expert System. IPMM ’97Australia-Pacific Forum on Intelligent Processing andManufacturing of Materials, Australia.
Liu, Bing. 1994. Problem Acquisition in Scheduling Domains.Expert System with Applications 6 : 257 – 265.
Pierre, Baptiste; Bruno, Legeard; Marie-Ange, Manier; andChristiphe, Varnier. 1994. A scheduling Problem OptimizationSolved with Constraint Logic Programming. ArtificialIntelligence 42 : 200 – 231.
Prosser, Patrick. Domain filtering can degrade intelligentbacktracking search. 1993. International Joint Conference onArtificial Intelligence : 262 - 267,
