A DISTRIBUTED MULTI-AGENT SYSTEM TO SOLVE AIRLINE

OPERATIONS PROBLEMS

Antonio Castro and Eugenio Oliveira

LIACC-NIAD&R, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal

Keywords: Distributed Multi-Agent Systems, Airline Operations Control, Operations Recovery, Disruption

Management.

Abstract: An airline schedule very rarely operates as planned. Problems related with aircrafts, crew members and

passengers are common and the actions towards the solution of these problems are usually known as

operations recovery or disruption management. The Airline Operations Control Center (AOCC) tries to

solve these problems with the minimum impact in the airline schedule, with the minimum cost and, at the

same time, satisfying all the required safety rules. Usually, each problem is treated separately and some

tools have been proposed to help in the decision making process of the airline coordinators. In this paper we

present the implementation of a Distributed Multi-Agent System (MAS) that represents the several roles

that exist in an AOCC. This MAS deals with several operational bases and for each type of operation

problems it has several specialized software agents that implements heuristic solutions and other solutions

based in operations research mathematic models and artificial intelligence algorithms. These specialized

agents compete to find the best solution for each problem. We present a real case study taken from an

AOCC where a crew recovery problem is solved using the MAS. Computational results using a real airline

schedule are presented, including a comparison with a solution for the same problem found by the human

operators in the Airline Operations Control Center. We show that, even in simple problems and when

comparing with solutions found by human operators in the case of this airline company, it is possible to find

valid solutions, in less time and with a smaller cost.

1 INTRODUCTION

One of the most important concerns in an airline

company is the Operations Control. Through

operations control mechanisms the airline company

monitors all the flights checking if they follow the

schedule that was previously defined by other areas

of the company. Unfortunately, some problems arise

during this phase (Kohl and Karish, 2004). Those

problems are related with crew members (for

example, a crew member that did not report for

duty), aircrafts (for example, a malfunction or a

delay due to bad weather) and passengers. When any

of these problems appear it is necessary to find

solutions for them. The Airline Operations Control

Centre (AOCC) is composed by teams of people

specialized in solving the above problems under the

supervision of an operation control manager. Each

team has a specific goal (for example, to guarantee

that each flight has the necessary crew members)

contributing to the common and general goal of

having the airline operation running with few

problems as possible. The process of solving these

problems is known as Disruption Management

(Kohl et al., 2004) or Operations Recovery.

Based on the observations we have done on an

AOCC of a real airline company we hypothesize that

the objective of solving the operations recovery

problems with the less cost as possible, will be much

easier to achieve if we include information in the

decision process related with various costs as well as

if we take advantage of the fact that airlines usually

have different operational bases with specific

resources. Regarding crew recovery problems, we

predict that if we take into account payroll

information like hour salary and perdiem value of

each crew rank, and costs related with hotels and

extra-crew travel between the different operational

bases, the solution will be less expensive. The same

principle can be applied to aircraft recovery and

passenger recovery if we use costs related with that

domain. We also hypothesize that the use of

different algorithms to solve the same problem (in

crew and aircraft recovery) will contribute to the

Castro A. and Oliveira E. (2007).

A DISTRIBUTED MULTI-AGENT SYSTEM TO SOLVE AIRLINE OPERATIONS PROBLEMS.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - AIDSS, pages 22-30

DOI: 10.5220/0002404100220030

 SciTePress

robustness of the system. We predict that using

different algorithms (genetic algorithms, heuristic,

etc.) in comparison with using always the same

algorithm, to solve the same problem, will permit to

always find the best solution (according to the

criteria defined by the company) and to always find

a solution, especially taking into account the fact

that we might benefit from solutions presented by

other operational bases.

In this paper we approach this problem so that it

can be solved by a Multi-agent System (MAS) that

represents the Operational Control Center of the

airline company. We use specialized agents, each

one implementing Artificial Intelligence algorithms,

simple heuristic algorithms and/or Operations

Research mathematical models, to find the best

solution to a specific problem related with crew,

aircrafts or passengers. We expect to obtain a

considerable decrease in the costs of the solutions

for the problems found when compared with the

costs of the solutions found by the current method

used in the airline we have observed. We also expect

that the heterogeneity of the algorithms, specialized

in different types of problems, will allow to find

solutions especially for the non-trivial problems,

contributing, in this way, to the robustness of the

system.

The rest of the paper is structure in the following

way. Section 2 presents some work of other authors

regarding operations recovery. Section 3 presents

our proposal of a MAS for airline operations

recover, including the architecture of the MAS, the

algorithm used to choose the best solution and an

example of the application of our MAS. Section 4

presents the scenario we setup to evaluate our

system as well as the results of the evaluation.

Section 5 presents the conclusion of our work.

2 STATE OF THE ART

Traditionally, the Operations Recovery Problem has

been solved through Operations Research (OR)

techniques. The paper (Barnhart et al., 2003) gives

on overview of OR applications in the air transport

industry. The literature that exists related with this

subject is usually divided according to the specific

resource to be recovered. The most common

division is by aircraft, crew and passengers.

However, it is also possible to find papers related

with more general approaches as well as related with

integrated recovery approaches. We will present

here the most recent published papers according to

(Clausen et al., 2005). We divided the papers in four

areas: general approaches, crew recovery, aircraft

recovery and integrated recovery. For a more

detailed explanation of the papers as well as for

older papers related with each of these subjects,

please consult (Clausen et al., 2005).

General Approaches

: In (Kohl et al., 2004) the

author’s reports on the experiences obtained during

the research and development of project

DESCARTES (a large scale project supported by

EU) on airline disruption management. The current

(almost manual) mode of dealing with recovery is

presented. They also present the results of the first

prototype of a multiple resource decision support

system.

Aircraft Recovery

: The most recent paper

considering the case of aircraft recovery is dated

from 2002 (Rosenberger et al., 2001). The proposed

model addresses each aircraft type as a single

problem. They formulate the problem as a Set

Partitioning master problem and a route generating

procedure. The goal is to minimize the cost of

cancellation and retiming, and it is the responsibility

of the controllers to define the parameters

accordingly. To solve the master problem in due

time, a heuristic is used to select only a subset of

aircraft to be involved in the Set Partitioning

problem. This approach results in running times

between 6 and 16 seconds for 3 real-size problem

instances. It is included in the paper a testing using

SimAir (Rosenberger et al., 2002) simulating 500

days of operations for three fleets ranging in size

from 32 to 96 aircraft servicing 139-407 flights.

Crew Recovery

: In (Abdelgahny et al., 2004) the

flight crew recovery problem for an airline with a

hub-and-spoke (a system of air transportation in

which local airports offers air transportation to a

central airport where long-distance flights are

available) network structure is addressed. The paper

details and sub-divides the recovery problem into

four categories: misplacement problems, rest

problems, duty problems, and unassigned problems.

Based on detailed information regarding the current

plan and pool of problems, the recovery problem is

solved in steps. Several means are used for recovery,

including delaying, swapping, deadheading (extra-

crew) and the use of stand by crew. The proposed

model is an assignment model with side constraints.

Due to the stepwise approach, the proposed solution

is sub-optimal. Results are presented for a situation

from a US airline with 18 problems.

Integrated Recovery

: It is uncommon to find

literature dedicated specifically to the passenger

recovery problem. We believe the main reason for

this is the fact that the passenger problems can be

minimized if we solve the aircraft and crew

problems. However, we would like to point out a

recent paper (Bratu and Barnhart, 2006) that,

although presenting an integrated recovery

approach, has a strong emphasis on reducing

A DISTRIBUTED MULTI-AGENT SYSTEM TO SOLVE AIRLINE OPERATIONS PROBLEMS

passenger arrival delays. This paper presents two

models that considers aircraft and crew recovery and

through the objective function focuses on passenger

recovery. These are based on the flight schedule

network. Although crew is incorporated into the

models they do not consider how to recover from

disrupted crews. To test the models an AOCC

simulator was developed, simulating domestic

operations of a major US airline. It involves 302

aircrafts divided into 4 fleets, 74 airports and 3 hubs.

Furthermore, 83869 passengers on 9925 different

passengers’ itineraries per day are used. Three

different scenarios with different levels of disruption

are presented. Execution times ranges from 201 to

5042 seconds. For all scenarios are generated

solutions with reductions in passenger delays and

disruptions.

Lettovsky’s Ph.D. thesis (Lettovsky, 1997) is the

first presentation of a truly integrated approach in

the literature, although only parts of it are

implemented. The thesis presents a linear mixed-

integer mathematical problem that maximizes total

profit to the airline while capturing availability of

the three most important resources: aircraft, crew

and passengers. The formulation has three parts

corresponding to each of the resources, that is, crew

assignment, aircraft routing and passenger flow. In a

decomposition scheme these three parts are

controlled by a master problem denominated the

Schedule Recovery Model.

Finally, we would like to point out a tool called

DART (Decision-Aided Rescheduling Tool)

(Martins and Morgado, 1996) that was developed to

control the flight operations of IBERIA (the Spanish

airline company). DART controls airline operations

by gathering real time world-wide information about

fleet and crew situation and providing decision

support for handling incidents. It covers the daily

execution of the ideal flight plan and is responsible

for tracking and solving any irregularities that might

arise during its execution. The authors claim that

DART has been able to solve some difficult

problems, proposing, in some cases, better solutions

than those proposed by the re-scheduling experts.

The paper does not present any comparative results.

3 A MAS FOR OPERATIONS

RECOVERY

3.1 General Description

As stated before we approached this problem by

developing a distributed multi-agent system (MAS)

that represents the Airline Operations Control Center

(AOCC). Some of the Agent/MAS characteristics

that make us adopt this paradigm are the following

(Wooldridge, 2002):

Autonomy

: MAS models problems in terms of

autonomous interacting component-agents, which

are a more natural way of representing task

allocation, team planning, and user preferences,

among others.

Distribution of resources

: With a MAS we can

distribute the computational resources and

capabilities across a network of interconnected

agents avoiding problems associated with

centralized systems.

Scalability

: A MAS is extensible, scalable,

robust, maintainable, flexible and promotes reuse.

These characteristics are very important in systems

of this dimension and complexity.

A high-level graphical representation of the

MAS architecture is presented in Figure 1.

Figure 1: MAS Architecture.

The square labeled BASE A shows the part of

the MAS that is installed in each operational base of

the airline company (e.g., NYC, LHR and LAX).

Each operational base has its own resources that are

represented in the environment, for example, Crew

Roster and Aircraft Roster are databases of

schedules for the crew members and aircrafts,

respectively. Other resources represented are the

airport information system (to be able to get

information regarding boarding gates and delays),

legacy systems (to access information regarding

costs, among others) and a knowledge database for

the learning capabilities of the MAS (this

characteristic of the MAS will not be explained in

ICEIS 2007 - International Conference on Enterprise Information Systems

this paper). Each operational base has also software

agents that represent roles in the AOCC. The Crew

Recovery Agent, Aircraft Recovery Agent and Pax

Recovery Agent are dedicated to solve crew, aircraft

and passengers problems, respectively, and should

be seen as sub-organizations inside the MAS. The

Apply Solution Agent applies the solution found and

authorized in the resources of the operational base.

Finally, each operational base has a Broker Agent

that is responsible for all the interactions between

other operational bases and the Electronic Market.

The MAS also has the possibility to interact with an

electronic market of airline resources such as

aircrafts and crew members, through the Company

Broker. According to (Kohl et al., 2004) “research

on recovery operation to this date only deals with a

single airline. Cooperation between airlines is not

supported”. With this approach we try to foster the

cooperation between airlines. More information

about this electronic market can be found in

(Malucelli et al., 2006). The MAS was developed

using JADE (Bellifemine et al., 2004) as

development platform and as the run-time

environment that provides the basic services for

agents to execute. The MAS was developed based

on a previous analysis and design by Castro and

Oliveira (Castro and Oliveira, 2005).

3.2 Sub-organization Architecture

As stated before, the Crew, Aircraft and Pax

Recovery agents as presented in Figure 1 should be

seen as sub-organizations. These sub-organizations

have their own architecture with their specialized

agents. Figure 2 shows the architecture for Crew

Recovery (corresponding to the Crew Recovery

Agent in figure 1) in a UML diagram. The

architecture for Aircraft Recovery and Pax Recovery

are very similar.

Figure 2: Crew Recovery sub-organization architecture.

The agent class OpMonitor is responsible for

monitoring any crew events, for example, crew

members that did not report for duty or duties with

open positions, that is, without any crew member

assigned to a specific role on board (e.g., captain or

flight attendant). When an event is detected, the

service MonitorCrewEvents will initiate the protocol

inform-crew-event (FIPA Request) informing the

OpCrewFind agent. The message will include the

information necessary to characterize the event. This

information is passed as a serializable object of the

type CrewEvent. Figure 3 shows the attributes of the

CrewEvent class.

Figure 3: Crew Events.

The OpCrewFind agent detects the message and

will start a CFP (call for proposal) through the crew-

solution-negotiation protocol (FIPA contractNET)

requesting to the specialized agents

HeuristicAlgorithm, AlgorithmA and AlgorithmB of

any operational base of the airline company, a list of

solutions for the problem. Each agent implements a

different algorithm specific for this type of problem.

When a solution is found a serializable object of the

type CrewSolutionList is returned in the message as

an answer to the CFP. Figure 3 shows the attributes

of the CrewSolutionList class. The OpCrewFind

agent collects all the proposals received and chooses

the best one according to the algorithm in Table 1:

Table 1: Multi-criteria algorithm.

foreach item in CrewSolution list

totalDuty = monthDuty+credMins

if (totalDuty-dutyLimit) > 0

credDuty = totalDuty-dutyLimit

else

credDuty = 0

end if

perdiemDays = (endDateTime-dutyDateTime

perdiemPay = perdiemDays*perdiemValue

dutyPay = credDuty*(hourSalaryValue/60)

cost = (dutyPay+perdiemPay)*baseFactor

end foreach

order all items by cost desc

select first item on the list

A DISTRIBUTED MULTI-AGENT SYSTEM TO SOLVE AIRLINE OPERATIONS PROBLEMS

The algorithm in Table 1 is implemented in the

service SendCrewSolution and produces a list

ordered by the cost (a multi-criteria cost) that each

solution represents. Table 2 explains each of the

computed values in the algorithm in Table 1.

Table 2: Computed values.

totalDut

Monthly duty minutes of the proposed

crew member after assigning the new duty

credDut

Number of minutes to be paid case the

crew member exceeds the monthly duty

limit

dutyPay Cost of duty computed according to the

hour salary of the crew member

perdiem

Days

Number of days of work for the specific

duty

perdiem

Pay

Cost of duty computed according to the

perdiem value of the crew member

base

Factor

If the crew member belongs to the same

operational base where the problem

happened, the value is equal to one.

Otherwise, it will have a value greater than

one.

Cost The sum of the cost of the perdiem plus

duty multiplied by the base factor.

The first solution of the list in descendant order by

cost corresponds to the less expensive one. The

SendCrewSolution service initiates the protocol

query-crew-solution-authorization (FIPA Query)

querying the OpManager agent for authorization.

The message includes the serializable object of the

type CrewSolution as shown in Figure 3.

3.3 Example

As a simple example, consider the following

situation: Airline Company A has two operational

bases, one in London (LHR) and another in Paris

(ORY), each with 150 crew members. On a specific

day a crew member of the London base did not

report for duty and it was necessary to find another

crew member to replace him. In our MAS the

OpMonitor agent of LHR base, would detect and

characterize the event according to Table 3.

The agent starts the inform-crew-event protocol

that includes the information from the CrewEvent,

informing the OpCrewFind agent. This agent starts a

CFP through the crew-solution-negotiation protocol

requesting all the solutions from the specialized

agents in both operational bases.

Table 3: Event characterization.

Attribute Value Comments

dutyDateTime 05-10-2006 10:00

delay 10 Crew is

delayed 10

mins.

dutyID NBPNC-1LHR19

endDateTime 05-10-2006 20:15 Duty end

date and time

readyDateTime 06-10-2006 09:15 Includes rest

credMins 615 Total work

time.

crewGrp 2 1=pilots;

2=flight att.

rank FA Crew rank

baseID LHR

crewNumber 97

crewName John

openPositions 1

eventID 1230 Internal

The OpCrewFind agent receives the solutions as

a CrewSolutionList from each agent according to

Table 4 (this table does not show all the information

that is included in the CrewSolutionList returned by

the agents, like for example, the crew number and

name).

Table 4: CrewSolutionList data.

base

rank hour

Salary

Value

per

diem

Value

duty

Limit

month

Duty

base

Factor

LHR FA 43 71 7800 7600 1

ORY FA 30 71 7800 8120 1,3

LHR FA 17 31 7800 8500 1

LHR FA 14 31 7800 7950 1

ORY FA 14 31 7800 5000 1,3

The service SendCrewSolution of agent

OpCrewFind computes the values indicated in Table

2 for each item of the CrewSolutionList and orders

them, according to the algorithm indicated in Table

1. The result of this operation is indicated in Table 5.

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Table 5: Ordered CrewSolutionList data.

Base

total

Duty

cred

Duty

duty

Pay

per

diem

Days

per

diem

Pay

cost

ORY 5615 0 0 1 31 40

LHR 8565 765 178 1 31 209

LHR 8215 415 297 1 71 368

LHR 9115 1315 372 1 31 403

ORY 8735 935 467 1 71 699

As it is possible to see, the solution with less cost

is solution number 5 (table 4) listed in first place in

table 5. In this particular example, it is a crew

member from a different operational base that is

considered the best solution to substitute the one that

did not report for duty. The SendCrewSolution

service initiates the protocol query-crew-solution-

authorization querying the OpManager for

authorization. The message includes the serializable

object CrewSolution with the complete information,

as presented in Table 6.

Table 6: CrewSolution serializable object.

Attribute Value

dutyID NBPNC-1LHR19

dutyDateTime 05-10-2006 10:00

endDateTime 05-10-2006 20:15

readyDateTime 06-10-2006 09:15

baseID ORY

crewGrp 2

rank FA

crewNumber 147

crewName Marie

seniority 15

dutyPay 0

perdiemPay 31

cost 40

4 SCENARIO AND

EXPERIMENTS

4.1 Scenario

To evaluate our MAS we have setup a scenario that

includes 3 operational bases (A, B and C). Each base

includes their crew members each one with a

specific roster. The data used corresponded to the

real operation of June 2006 of base A. We have

simulated a situation where 15 crew members, with

different ranks, did not report for duty in base A.

The events did not happen at the same day and each

one corresponds to a crew member that did not

report for a specific duty in a specific day.

After setting-up the scenario we found the

solutions for each crew event using two methods. In

the first method

we used a real user from the AOCC,

with the current tools available, to find the solutions.

The user uses software that shows the roster of each

crew member in a Gantt chart for a specific period.

The user can scroll down the information, filter

according to the crew rank and base, and sort the

information by name, month duty, etc. Each user has

a specific way of trying to find the solutions.

However, we have observed that, in general, they

follow these steps:

First

: Open the roster for a one month period,

starting two days before the current day.

Second

: Filter the roster by crew rank and base,

where the base is equal to the base where the crew

event happened and crew rank is equal to the crew

member that did not report for duty.

Third

: Order the information by month duty, in an

ascendant order and by seniority in a descendent

order.

Fourth

: Visually, they scroll down the information

until they found a crew member with an open space

for the period of time that corresponds to the duty to

be assigned. This period of time takes into

consideration the start and end time of the duty and

also the time required for resting (ready date time).

Fifth

: If they do not found a crew member in the

base specified, they try to find it in another base,

filtering the information accordingly.

Sixth

: They assign the duty to the first crew member

they found.

In the second method

we have used the sub-

organization Crew recovery of our MAS as indicated

in Section 3.2.

4.2 Results

The data (partial) obtained using method 1 is

presented in Table 7 and the data obtained using

method 2 in Table 8. We point out that the data in

columns marked with an asterisk were calculated

manually, according to the formulas in the algorithm

presented in Table 1. The reason for this is that the

information system that is available for the users

does not include information related with any kind

of payroll.

A DISTRIBUTED MULTI-AGENT SYSTEM TO SOLVE AIRLINE OPERATIONS PROBLEMS

Table 7: Solutions obtained through method 1.

Base

time

(sec)

rank duty

Pay (*)

perdiem

Pay (*)

cost

(*)

A 90 FA 0 72 72

B 115 FA 0 72 86,4

A 75 CPT 942,9 106 1048,9

A 100 FA 939 144 1083

B 120 FA 0 72 86,4

B 100 CPT 777 212 1186,8

B 105 FO 0 148 177,6

A 80 SFA 687,65 72 759,65

B 110 SFA 0 144 172,80

C 110 CPT 0 212 296,8

A 110 FA 0 72 72

C 120 FA 0 72 100,8

B 115 FA 0 72 86,4

Table 8: Solutions obtained through method 2.

Base

time(sec) rank duty

Pay

perdiem

Pay

cost

A 20 FA 0 72 72

B 31 FA 0 72 86,4

B 18 CPT 0 106 127,2

C 27 FA 563,4 62 875,6

B 32 FA 0 72 86,4

C 26 CPT 0 212 296,8

A 25 FO 0 144 144

B 15 SFA 229,17 72 361,4

B 29 SFA 0 144 172,8

C 23 CPT 0 212 296,8

A 27 FA 0 72 72

C 31 FA 0 72 100,8

B 32 FA 0 72 86,4

The Solution

part of table 9 shows the number of

solutions found in each operational base. The Time

part of the table shows how long it took to find the

solutions in each base. The Total Costs

shows the

cost of the solutions by each of the crew ranks

involved: CPT (Captains), FO (First Officers), SFA

(Senior Flight Attendants) and FA (Flight

Attendants). It also shows the costs by each of the

operational bases.

Table 9 presents the results that compare the two

methods.

5 CONCLUSIONS

From the results we can see that our MAS obtains

valid solutions faster and with less costs when

compared with the current method used in a real

airline company. Regarding our first hypothesis we

were expecting a considerable decrease in the costs

of the solutions found by our MAS. From the results

obtained (see Table 9) we can see that:

Table 9: Comparison of the results.

Method 1 Method 2 Met1/

Met2

Total % Total % %

Solution:

- Base A 7 47 3 20,0 -57,1

- Base B 6 40 7 47,0 16,7

- Base C 2 13 5 33,0 150,0

Time (sec) 101 100 25 24,8 -75,3

- Base A 88 21 24 24,0 -72,7

- Base B 110 27 24 24,0 -78,2

- Base C 115 28 26 26,0 -77,4

Total

Costs:

7039,6 100 3839,3 54,5 -45,5

Costs

Rank:

- CPT 2532,5 36,0 720,8 18,8 -71,5

- FO 720,0 10,2 499,2 13,0 -30,7

- SFA 932,5 13,3 534,2 13,9 -42,7

- FA 2854,6 40,6 2085,1 54,3 -27,0

Costs Base:

- Base A 4845,5 92,4 288,0 11,2 -94,0

- Base B 1796,4 34,3 1275,8 49,8 -29,0

- Base C 397,6 7,6 2275,5 88,8 472,3

Cost(SolutionMAS(3839,3))<Cost(SolutionMet1(7039,6))

It represents a decrease of 45.5% on the costs.

Our hypothesis was accepted. Of course that we

cannot infer that our MAS will always produce

solutions that cost 45.5% less. It is not even possible

to say that, in average, this decrease is valid. For that

we need to evaluate much more situations, in

different times of the year (we might have seasoned

behaviours) and, then, find an average value.

However, taking into consideration that our method

includes information that is not available in the

current method of the airline (for example, hour

salary, perdiem value, lodging and extra-crew

travel), and that this information has an immense

impact on the total cost, we can state that our

method will never produce more expensive

solutions.

From the results we can also obtain other

interesting conclusions. These conclusions can be

expressed by the following formalism:

1. Time(SolutionMAS(25s))<

Time(SolutionMet1(101s))

2. Cooperation(SolutionMAS(BaseB(47%))) >

Cooperation(SolutionMet1(BaseB(40%))) and

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Cooperation(SolutionMAS(BaseC(33%))) >

Cooperation(SolutionMet1(BaseC(13%)))

Regarding 1) our method was 75.3% faster than

method two. The use of a computerized system to

find and evaluate

the solutions is the reason for our

method to be faster than the present method used in

the airline. Regarding 2) we can see that the

cooperation between different operational bases has

increased with our method, because we evaluate all

the solutions found (including the ones from

different operational bases where the event

happened) and choose the one with less cost. In

method one, they choose the first one they find,

usually from the same base where the event was

triggered. This cooperation is also possible to be

inferred from the costs by base. In Table 9 it is

possible to see that the costs of base C had an

increase of 472.32% while base A and base B

decreased 94% and 29%, respectively. This means

that our method used more resources from other

bases than the base where the problem happened

(base A).

Regarding our second hypothesis we expected to

increase the robustness of our system using

heterogeneous algorithms to find solutions to the

same problem, at the same time. We were not able to

collect enough data to analyze the impact on

robustness as the result of using different specialized

agents. Preliminary results show that, most of the

times, the MAS presents at least one solution even

when the human operator cannot found one.

Apparently this is the result of using different

techniques to tackle the problem. However, the

solution might have a cost that, when compared with

other ways of solving the problem (for example,

cancelling the flight), might be unacceptable. This

tells us that our MAS need to have access to more

information. For example and in the case of

cancelling the flight, it would be important to have

access to the cost of compensations due to

passengers in these situations. In the future we will

try to prove this hypothesis.

This paper has presented a distributed multi-

agent system as a possible solution to solve airline

operations recovery problems, including sub-

organizations with specialized agents, dedicated to

solve crew, aircraft and passenger recovery

problems. We have detailed the architecture of our

MAS regarding the sub-organization dedicated to

solve crew recovery problems, including agents,

services and protocols. We have introduced a multi-

criteria algorithm for selecting the solution with less

cost from those proposed as part of the negotiation

process. A simple example was presented,

following, step-by-step, our proposed method. A

case study, taken from a real scenario in an airline

company where we tested our method was also

presented and we discuss the results obtained. We

have shown that our method produces faster and less

expensive solutions when compared with the present

method used in the airline company.

Further work is required in testing our method

for large periods of time and in different times of the

year (due to seasoned behaviours). We also need to

test our MAS with all the sub-organizations working

at the same time (crew, aircraft and passenger) to see

the impact that might exist in the results we have

presented in this paper. Finally, we would like to

apply and test the integration of the EM as presented

in (Malucelli et al., 2006).

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