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
19
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
Copyright
c
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
ICEIS 2009 - International Conference on Enterprise Information Systems
20
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
21
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
2
+1.38*x
Pleasure y = 1.20*x
Family y = 1.15*x
Illness y = 0.06*x
2
+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.
ICEIS 2009 - International Conference on Enterprise Information Systems
22
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
2
+1.38*fltDelay
iPfCost = 0.06*fltDelay
2
+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
23
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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