USING QUALITY COSTS IN A MULTI-AGENT SYSTEM

FOR AN AIRLINE OPERATIONS CONTROL

Antonio J. M. Castro and Eugenio Oliveira

Informatics Engineering Department and LIACC/NIAD&R, Faculty of Engineering, University of Porto, Portugal

Keywords: Airline Operations Control, Operations Recovery, Quality Costs, Multi-Agent System, Operational Costs.

Abstract: The Airline Operations Control Centre (AOCC) tries to solve unexpected problems that might occur during

the airline operation. Problems related to aircrafts, crewmembers and passengers are common and the

actions towards the solution of these problems are usually known as operations recovery. Usually, the

AOCC tries to minimize the operational costs while satisfying all the required rules. In this paper we present

the implementation of a Distributed Multi-Agent System (MAS) representing the existing roles in an

AOCC. This MAS has several specialized software agents that implement different algorithms, competing

to find the best solution for each problem that include not only operational costs but, also, quality costs so

that passenger satisfaction can be considered in the final decision. We present a real case study where a

crew recovery problem is solved. We show that it is possible to find valid solutions, with better passenger

satisfaction and, in certain conditions, without increasing significantly the operational costs.

1 INTRODUCTION

Operations control is one of the most important

areas in an airline company. 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 (Clausen et al., 2005). Those

problems are related to crewmembers, aircrafts and

passengers. 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 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. To be able to choose the best solution to a

specific problem, it is necessary to include the

correct costs on the decision process. It is possible to

separate the costs in two groups: Operational Costs

(easily quantifiable costs) and Quality Costs (less

easily quantifiable costs). The operational costs are,

for example, crew costs (salaries, hotel, extra-crew

travel, etc.) and aircraft/flights costs (fuel, approach

and route taxes, handling services, line maintenance,

etc.). The quality costs that we are interested in

calculating in the AOCC domain are, usually, related

to passenger satisfaction. Specifically, we want to

include in the decision process the cost of delaying

or cancelling a flight from the passenger point of

view, that is, in terms of the importance that a delay

will have to the passenger. In (Castro and Oliveira,

2007) the authors presented a Distributed Multi-

Agent System (MAS) to solve airline operations

problems that included operational costs but did not

take into consideration the quality costs we

mentioned before. Starting from this work and based

on our observations we have done on an AOCC of a

real airline company we hypothesize that the

inclusion of quality costs in the decision process will

increase the customer satisfaction (a fairly obvious

prediction) without increasing significantly (or

nothing at all) the operational costs of the solutions

in a given period. Basically, we expect to find valid

alternate solutions within the same operational cost

but with a better impact on the passenger

satisfaction.

In this paper we show how we changed the MAS

presented in (Castro and Oliveira, 2007) specifically

how we improved the specialized agents to include

the quality costs on the decision process. The rest of

the paper is organized as follows. In section 2 we

Castro A. and Oliveira E. (2009).

USING QUALITY COSTS IN A MULTI-AGENT SYSTEM FOR AN AIRLINE OPERATIONS CONTROL.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Artiﬁcial Intelligence and Decision Support Systems, pages

19-24

DOI: 10.5220/0001849800190024

 SciTePress

present some work of other authors regarding

operations recovery. Section 3 shows how we

arrived at the formulas we have used to express the

importance of the flight delay, from the passenger

point of view. Section 4 shows how we have

updated the MAS presented in (Castro and Oliveira,

2007) to include quality costs, including the MAS

architecture and the algorithm used to choose the

best solution. In section 5 we present the scenario

used to evaluate the system as well as the results of

the evaluation. Finally, we discuss and conclude our

work in section 6.

2 RELATED WORK

Traditionally, the Operations Recovery Problem has

been solved through Operations Research (OR)

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

an overview of OR applications in the air transport

industry. We divided the papers in three areas: 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).

Aircraft Recovery. The most recent paper

considering the case of aircraft recovery is

(Rosenberger et al., 2001). 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. 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. Although the authors do try to minimize

the flights delays, nothing is included regarding the

use of quality costs.

Crew Recovery. In (Abdelgahny et al., 2004) the

flight crew recovery problem for an airline with a

hub-and-spoke 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.

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. This work omits the use of quality costs.

Integrated Recovery. In (Bratu and Barnhart, 2006)

the author presents two models that considers

aircraft and crew recovery and through the objective

function focuses on passenger recovery. They

include delay costs that capture relevant hotel costs

and ticket costs if passengers are recovered by other

airlines. According to the authors, it is possible to

include, although hard to estimate, estimations of

delay costs to passengers and costs of future lost

ticket sales. 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. For all scenarios are

generated solutions with reductions in passenger

delays and disruptions. The difference regarding our

proposal is that we use the opinion of the passengers

when calculating the importance of the delay.

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. Although the author

takes into consideration the passenger, it does so

regarding finding the best solution for the disrupted

passengers. The difference regarding our approach is

that we use the opinion of the passengers regarding

the delay (expressed through a mathematical

formula) to make the best solution regarding

delaying the flight. We do not approach the also

important issue of finding the best itinerary for

disrupted passengers.In (Castro and Oliveira, 2007)

the author presents a Multi-Agent System (MAS) to

solve airline operations problems, using specialized

agents in each of the three usual dimensions of this

problem: crew, aircraft and passengers. The MAS

represents the Airline Operations Control Centre

(AOCC) and is able to deal with different

operational bases (geographically distributed) each

with its own resources. The architecture and the

specialized agents of the crew recovery sub-

organization are presented as well as a case study of

how the MAS solved several crew related problems

during a one-month period. However, in the

examples presented, the author ignores the impact

that a delay in the flight might have on the decision

process and only use operational costs to make the

best decision. That is the biggest difference

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regarding the work we present in this paper. We start

from this approach and make the necessary changes

on the specialized agents and in the multi-criteria

algorithm, so that the quality costs are included. For

those interested in agent-oriented methodologies and

in how this MAS was developed, please read (Castro

and Oliveira, 2008).

3 QUALITY COSTS IN AOC

3.1 How to Quantify

Overview. The Airline Operations Control Centre

(AOCC) has the mission of controlling the execution

of the airline schedule and, when a disruption

happens (aircraft malfunction, crewmember missing,

etc.) find the best solution to the problem. It is

generally accepted that, the best solution, is the one

that does not delay the flight and has the minimum

operational cost. In summary, it is the solution that is

nearest to the schedule, assuming that the schedule is

the optimal one. Unfortunately, due to several

reasons (see (Kohl and Karish, 2004) for several

examples), it is very rare to have available solutions

that do not delay a flight and/or do not increase the

operational cost. From the observations we have

done in a real AOCC, most of the times, the team of

specialists have to choose between available

solutions that delay the flight and increase the

operational costs. Reasonable, they choose the one

that minimize these two values.

The Perception of Quality Costs. In our

observations, we found that some of the teams in the

AOCC, used some kind of rule of thumb or hidden

knowledge and, in some cases, they did not choose

the solutions that minimize the delays and/or the

operational costs. For example, suppose that they

have disruptions for flight A and B with similar

schedule departure times. The best solution to flight

A would cause a delay of 30 minutes and the best

solution to flight B would cause a delay of 15

minutes. Sometimes, and when technically possible,

they would prefer to delay flight A in 15 minutes

and flight B in 30 minutes or more if necessary. We

can state that flights with several business

passengers, VIP’s or for business destinations

correspond to the profile of flight A in the above

example. In our understanding this means that they

are using some kind of quality costs when taking the

decisions, although not quantified and based on

personal experience. In our opinion this makes the

decision less reliable but that knowledge, represents

an important part in the decision process and should

be included on it.

Quantifying Quality Costs. To be able to use this

information in a reliable decision process we need to

find a way of quantifying it. What we are interested

to know is how the delay time and the importance of

that delay to the passenger are related in a specific

flight. It is reasonable to assume that, for all

passengers in a flight, less delay is good and more is

bad. However, when not delaying is not an opinion

and the AOCC has to choose between different

delays to different flights, which ones should they

choose? To be able to quantify this information, we

have done a survey to several passengers on flights

of an airline company. Besides asking in what class

they were seated and the reason for flying in that

specific flight, we asked them to evaluate from 1 to

10 (1 – not important, 10 very important) the

following delay ranges (in minutes): less that 30,

between 30 and 60, between 60 and 120, more than

120 and flight cancellation. From the results we

found the passenger profiles in table 1.

Table 1: Passenger profiles.

Profiles Main Characteristics

Business Travel in first or business class; VIP’s; Frequent

Flyer members; Fly to business destinations;

More expensive tickets;

Pleasure Travel in economy class; Less expensive tickets;

Fly to vacation destinations;

Family Usually immigrants; Fly to/from destinations

with immigrants communities; Fly to see family

and/or to go to funerals; Travel in economy

class;

Illness Stretcher on board; Medical doctor or nurse

travelling with the passenger; Personal oxygen

on board or other special needs;

The most important information that we want to

get from the survey data is the trend of each profile,

regarding delay time/importance to the passenger.

Plotting the data and the trend we got the graph in

figure 1 (x – axis is the delay time and y – axis the

importance).

Figure 1: Delay Time vs Importance.

USING QUALITY COSTS IN A MULTI-AGENT SYSTEM FOR AN AIRLINE OPERATIONS CONTROL

From the graph in figure 1 it is possible to see

the equations that define the trend of each profile. If

we apply these formulas as is, we would get quality

costs for flights that do not delay. Because of that we

re-wrote the formulas. The final formulas that

express the importance of the delay time for each

passenger profile are presented in table 2.

Table 2: Final quality costs formulas.

Profile Formula

Business y = 0.16*x

+1.38*x

Pleasure y = 1.20*x

Family y = 1.15*x

Illness y = 0.06*x

+1.19*x

3.2 Using Quality Costs

in Operations Recovery

Overview. The MAS we used is a modification of

the one used in (Castro and Oliveira, 2007) and

represents the Airline Operations Control Centre

(AOCC).

In the MAS, 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 crewmembers and

aircrafts, respectively. Other resources represented

are the airport information system, legacy systems

and a knowledge database for the learning

capabilities of the MAS. 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.

Architecture and Specialized Agents. The MAS

sub-organizations have their own architecture with

their specialized agents. Figure 2 shows the

architecture for Crew Recovery in a UML diagram.

The architecture for Aircraft Recovery and Pax

Recovery are very similar. The agent class

OpMonitor is responsible for monitoring any crew

events, for example, crewmembers that did not

report for duty or duties with open positions, that is,

without any crewmember 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.

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 (or

any other that is implemented and deployed in the

MAS) 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 2: Crew Recovery Architecture.

The OpCrewFind agent collects all the proposals

received and chooses the best one according to the

algorithm in Table 3. This algorithm is implemented

in the service SendCrewSolution and produces a list

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

solution represents. Some of the computed values in

the algorithm in Table 3 are the following (see

(Castro and Oliveira, 2007) for more information):

oCost

: The operational cost of the solution.

pBus

: The total of passengers in the business profile

on the disrupted flight.

pFam

: The total of passengers in the family profile

on the disrupted flight.

pIll

: The total of passengers in the illness profile on

the disrupted flight.

pPleasure

: The total of passengers in the pleasure

profile on the disrupted flight.

bPfCost

: The importance of the delay for each

passenger of the business profile.

iPfCost

: The importance of the delay for each

passenger of the illness profile.

pPfCost

: The importance of the delay for each

passenger of the pleasure profile.

fPfCost

: The importance of the delay for each

passenger of the family profile.

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qCost: The quality cost of the solution.

It is important to point out the use of coefficient

C1 in the quality cost formula. The goal of this

coefficient is to give a value to the quality costs in

the same unit of the operational costs. Operational

costs are expressed in monetary units (Euros,

Dollars, etc.) because they are direct and real costs.

On the other hand, quality costs are not real costs

and express a level of satisfaction of the passengers.

Besides transforming the quality costs into a

monetary unit, airline companies can also use this

coefficient to express the importance that this type

of cost has in the decision process, by increasing its

value.

Table 3: Multi-criteria algorithm.

foreach item in CrewSolution list

tDuty = monthDuty+credMins

if (tDuty-dutyLimit) > 0

cDuty = tDuty-dutyLimit

else

cDuty = 0

end if

pDays = (endDateTime-dutyDateTime)+1

pPay = pDays*perdiemValue

dPay = cDuty*(hourSalaryValue/60)

oCost = (dPay+pPay)*bFactor

pBus = cPax+vipPax+fflyerPax+paxTot*busDest

pFam = yPax+paxTot*imigDest

pIll = illPax

pPleasure = yPax+paxTot*vacDest

bPfCost = 0.16*fltDelay

+1.38*fltDelay

iPfCost = 0.06*fltDelay

+1.19*fltDelay

pPfCost = 1.2*fltDelay

fPfCost = 1.15*fltDelay

qCost = C1*(bPfCost*pBus+iPfCost*pIll +

pPfCost * pPlea + fPfCost * pFam)

totalCost = oCost+qCost

end foreach

order all items by totalCost desc

select first item on the list

The first solution of the list in descendant order

by cost is selected. 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.

4 EXPERIMENTS

Scenario. To evaluate our MAS we have setup the

same scenario used by the authors in (Castro and

Oliveira, 2007) that include 3 operational bases (A,

B and C). Each base includes their crewmembers

each one with a specific roster. The data used

corresponded to the real operation of June 2006 of

base A. After setting-up the scenario we found the

solutions for each crew event using our Crew

Recovery Architecture and Specialized Agents of

our MAS. As a final step, the solutions found by our

MAS were presented to AOCC users to be validated.

Results. Table 4 presents the results that compare

our method (method B) with the one used by the

authors in (Castro and Oliveira, 2007), updated with

quality costs for a better comparison (method A).

We point out that in method A the quality costs were

not used to find the best solution. From the results

obtained we can see that on average, method B

produced solutions that decreased flight delays in

36%.

Table 4: Comparison of the results.

Method A Method B A/B

Total % Total % %

Delay (avg): 11 100 7 64 -36

Time (avg) 25 100 26 103 3

Total Costs:

11628

100 8912 77 -23

Oper. Costs: 3839 100 4130 108 8

Qual. Costs: 7789 100 4782 61 -39

Regarding the total costs (operational + quality),

the method B has a total cost of 8912 and method A

a total cost of 11628. Method B is, in average 3%

slower than method A in finding a solution and

produces solutions that represent a decrease of 23%

on the total costs. Regarding operational costs,

method A has a cost of 3839 and method B a cost of

4130. Method B is 8% more expensive regarding

operational costs. Regarding quality costs, method A

has a cost of 7789 and method B a cost of 4782.

Method B is 38% less expensive regarding quality

costs.

5 CONCLUSIONS

Regarding our first hypothesis we were expecting

that the inclusion of quality costs would increase

customer satisfaction. This is a fairly obvious

conclusion. The quality costs we present here

measure the importance of flight delays to the

passengers and this is one of the most important

quality items in this industry. If we decrease delays

we are increasing passenger satisfaction. Regarding

hypothesis two we were expecting to increase the

passenger satisfaction without increasing

significantly (or nothing at all) the operational costs

USING QUALITY COSTS IN A MULTI-AGENT SYSTEM FOR AN AIRLINE OPERATIONS CONTROL

in a given period. From the results in table 4 we can

see that operational costs increased 8% when

comparing with the method used by (Castro and

Oliveira, 2007). If we read this number as is we have

to say that our hypothesis is false. An 8% increased

on operational costs can represent a lot of money.

However, we should read this number together with

the flight delay figure. As we can see, although

method B increased the operational costs in 8% it

was able to choose solutions that decrease, in

average, 36% of the flight delays. This means that,

when there are multiple solutions to the same

problem, our method is able to choose the one with

less operational cost, less quality costs (hence, better

passenger satisfaction) and, because of the relation

between quality costs and flight delays, the solution

that produces less flight delays. From this

conclusion, one can argue that if we just include the

operational costs and the expected flight delay,

minimizing both values, the same results can be

achieved having all passengers happy. In general,

this assumption might be true. However, when we

have to choose between two solutions with impact

on other flights, which one should we choose? In our

opinion, the answer depends on the profile of the

passengers of each flight and on the importance they

give to the delays, and not only in minimizing the

flight delays. Our method takes into consideration

this important information when taking decisions.

This paper has presented an improved version of

the distributed multi-agent system in (Castro and

Oliveira, 2007) 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, which take into consideration the

passenger satisfaction in the decision process. We

have introduced a process of calculating the quality

costs that, in our opinion, represents the importance

that passengers give to flight delays. We show how,

through a passenger survey, we build four types of

passenger profiles and, for each one of these

profiles, how we calculate a formula to represent

that information. We have introduced an updated

multi-criteria algorithm for selecting the solution

with less cost from those proposed as part of the

negotiation process, taking into consideration the

quality costs. A case study, taken from a real

scenario in an airline company where we tested our

method was presented and we discuss the results

obtained. We have shown that our method is able to

choose solutions that contribute to a better passenger

satisfaction and that produce less flight delays when

compared with a method that only minimizes

operational costs.

ACKNOWLEDGEMENTS

This work is supported by FCT research grant

SFRH/BD/44109/2008. The authors are grateful to

TAP Portugal for allowing the use of real data from

the company.

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ICEIS 2009 - International Conference on Enterprise Information Systems