Antonio Castro

Through operations control mechanisms an airline company monitors all the flights checking if they follow the schedule that was previously defined by other areas of the company. Some problems may arise during this stage related with crew members, aircrafts and passengers. The Airline Operations Control Centre (AOCC) includes teams of experts specialized in solving the above problems, seeking for solutions that minimize the negative impact on passengers and, at the same time, minimizing the operational costs. This book proposes a Multi-Agent System (MAS) that represents the AOCC allowing the airline company to take faster and better decisions when solving operational problems, taking into consideration the operational costs involved. It shows how such a system can be designed using an Agent-Oriented Software Engineering (AOSE) methodology and presents the implementation of the system in a real airline company, using widely available tools. For airline professionals it shows how Artificial Intelligence (AI) can help to solve such a critical problem. For students and AI/AOSE researchers shows the rationale behind the development of a real-world MAS.

"Airline companies make a huge effort to maximize their revenue while keeping their costs at a minimum. Unfortunately, any operational plan has a strong probability of being affected, not only by large disruptions like the one that happened in April 2010 due to the eruption of the Iceland Eyjafjallajökull volcano but, more frequently, by smaller daily disruptions caused by bad weather, aircraft malfunctions and crew absenteeism, for example.
These disruptions affect the original schedule plan, delaying the flights, and cause what is called an Irregular Operation. Studies have estimated that irregular operations can cost between 2% and 3% of the airlines' annual revenues and that a better recovery process could result in cost reductions of at least 20% of its irregular operations.
In this thesis, we have studied the AOCC of TAP Portugal as well as the work of other researchers in this field in order to propose a distributed and decentralized general approach to integrated and dynamic disruption management in airline operations control, based on the Multi-Agent System (MAS) paradigm.
The approach is distributed because it allows the functional, spatial and physical distribution of the intervening agent roles and the environment; it is decentralized because some decisions are made in different nodes of the agents' network; it is integrated because it includes the main dimensions of the problem: aircraft, crew and passengers; and it is dynamic because, in real time, several agents are performing in the environment, reacting to constant change.
The results show that our proposal, not only corroborates existing studies regarding the possible cost reductions that could result from a better disruption management process but, also, gives the possibility of reaching solutions that balance the utility of the three dimensions of the problem: aircraft, crew and passengers."

Studies have estimated that irregular operations
(flights affected by a disruption) can cost between 2% and
3% of the airline annual revenue and that a better recovery
process could result in cost reductions of at least 20%. Even
for small airlines this can represent millions of Euros. In this
paper we propose a multi-agent system (MAS) whose members
represent the roles, functionalities and competences existing in a
typical Airline Operations Control Centre (AOCC), the airline
entity responsible for managing the impact of irregular events
on planned operations. This multiagent based system produces
intelligent solutions in the sense that its outcomes are the result
of an autonomous reaction and adaption to changes in the environment, solving partial problems simultaneously. We tested
our MAS using real data from TAP Portuguese airline company
and experimentally compared our system with solutions found
by the human operators on TAP Portugal AOCC. A comparison
was also made with a more traditional sequential approach
that is the typical method followed by AOCCs when solving
disruptions. Results from those comparisons show that it is
possible to reduce costs and have a better integrated solution
with the proposed system.

The Airline Operations Control Centre (AOCC) of an airline company is the organization responsible for monitoring and solving operational problems. It includes teams of human experts specialized in solving problems related with aircrafts, crewmembers, and passengers, in a process called disruption management or operations recovery. In this article, the authors propose a new concept for disruption management in this domain. The organization of the AOCC is represented by a multi-agent system (MAS), where roles that correspond to the most frequent tasks that could benefit from a cooperative approach, are performed by intelligent agents. The human experts, represented by agents that are able to interact with them, are part of this AOCC-MAS supervising the system and taking the final decision from the solutions proposed by the AOCC-MAS. The authors show the architecture of this AOCC-MAS, including the main costs involved and details about how the system takes decisions. They tested the concept, using several real airline crew-related problems and using four methods: human experts (traditional way), the AOCC-MAS with and without using quality-costs, and the integrated approach presented in this article. The results are presented and discussed.

The Airline Operations Control Centre (AOCC) of an airline company is the organization responsible for monitoring and solving operational problems. It includes teams of human experts specialized in solving problems related with aircrafts, crewmembers and passengers, in a process called disruption management or operations recovery. In this chapter we propose a new concept for disruption management in this domain. The organization of the AOCC is represented by a multi-agent system (MAS), where the roles that correspond to the most repetitive tasks are performed by intelligent agents. The human experts, represented by agents that are able to interact with them, are part of this AOCC-MAS supervising the system and taking the final decision from the solutions proposed by the AOCC-MAS. We show the architecture of this AOCC-MAS, including the main costs involved and details about how the system takes decisions. We tested the concept, using several real airline crew related problems and using four methods: human experts (traditional way), the AOCC-MAS with and without using quality-costs and the integrated approach presented in this chapter. The results are presented and discussed.

Operations control is one of the most important areas for an airline company. Through operations control mechanisms an 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 may arise during this stage (Clausen et al., 2005). Those problems can be related with crewmembers, aircrafts and passengers. The Airline Operations Control Centre (AOCC) includes teams of experts 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 under as few problems as possible. The process of solving these kinds of problems is known as Disruption Management (Kohl et al., 2004) or Operations Recovery.
To select the best solution to a specific problem, it is necessary to include the actual costs in the decision process. One can separate the costs in two categories: Direct Operational Costs (easily quantifiable costs) and Quality Operational Costs (less easily quantifiable costs). Direct operational costs are, for example, crew related costs (salaries, lodgement, extra-crew travel, etc.) and aircraft/flights cost (fuel, approach and route taxes, handling services, line maintenance, etc.). The quality operational costs that AOCC is interested in calculating are, usually, related with passengers satisfaction. Specifically, we want to include in the decision process the estimated cost of delaying or cancelling a flight from the passenger point of view, that is, in terms of the importance that such a delay will have to the passenger.
In this chapter we present our intelligent agent-based approach to help the AOCC solving the disruption management problem.

When recovering from operational problems, the Airline Operations Control Centre (AOCC) usually tries to minimize direct 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 real-life roles in an AOCC. This MAS includes software agents that cooperate through a distributed problem solving approach, to find the best solution for each problem. We propose a general approach to quantify quality operational costs, so that passengers’ satisfaction can also be considered in the final decision. We present a real case study to introduce our approach to quantify the quality operational costs and solve several real unexpected crew problems. We show that our MAS with quality costs is able to reduce flight delays and increase passenger satisfaction without increasing significantly the direct operational costs. A comparison with two other methods is presented.

Airports are important infra-structures for the air transportation business. One of the major operational constraints is the peak of passengers in specific periods of time. Airline companies take into consideration the airport capacity when building the airline schedule and, because of that, the execution of the airline operational plan can contribute to improve or avoid airport peak problems. The Airline Operations Control Center (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. In this paper we propose a way of measuring the AOCC performance that takes into consideration the relation that exists between airline schedule and airport peaks. The implementation of a Distributed Multi-Agent System (MAS) representing the existing roles in an AOCC, is presented. We show that the MAS contributes to minimize airport peaks without increasing the operational costs of the airlines.

The Airline Operations Control Centre (AOCC) tries to solve unexpected problems during the airline operation. Problems with aircraft, crewmembers and passengers are common and very hard to solve due to the several variables involved. This paper presents the implementation of a real-world multi-agent system for operations recovery in an airline company. The analysis and design of the system was done following a GAIA based methodology. We present the system specification as well as the implementation using JADE. A case study is included, where we present how the system solved a real problem.

In this paper we introduce an implementation of an Intelligent Interface Agent to support the use of a web portal in an airline company. The interface agent architecture and data model is presented. We formalized concepts such as relevance and proximity regarding the data structure. The concepts of personal opinion and general opinion are also introduced and formalized. A statistical analysis was performed to obtain the best value when processing the general opinion. Some results of that analysis are presented and we conclude discussing our work and presenting future improvements.

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.

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. The Airline Operations Control Center (AOCC) tries to solve these problems with the minimum cost and 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.

An airline schedule seldom 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. We have observed the AOCC of TAP Portugal, the major Portuguese airline, and, from those observations, several hypotheses have been identified and some of them experimented. We believe, and that is one of our main hypothesis, that the Multi-Agent System (MAS) paradigm is more adequate to represent the multi-level hierarchy organization and the several roles that are played in an AOCC. In this thesis we propose the design and partial implementation of a Distributed MAS representing the existing roles in an AOCC. We hypothesize that if we take advantage of the fact that each operational base has specific resources (both crew and aircrafts) and that if we include information regarding costs involved (for example, crew payroll information and hotels costs, among others), the solutions to the detected problems will be faster to find and less expensive. We also hypothesize that if we use specialized software agents that implement different solutions (heuristic and other solutions based in operations research models and artificial intelligence algorithms), to the same problem, the robustness of the system will increase. Finally, we believe that the inclusion of some kind of learning mechanism that learns from previous utilization of crew members will improve the solutions quality. Extending that learning mechanism to learn each crew member profile, and applying that knowledge for generating future schedules, the management of that expensive resource will be much more efficient and the level of satisfaction of each crew member will increase. We also present a real case study taken from TAP Portugal 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 for 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. In this thesis we also show how we complement the GAIA methodology in order to better analyze and design the proposed MAS for the AOCC. Besides showing the rationale behind the analysis, design and implementation of our system, we also present how we mapped the abstractions used in agent-oriented design to specific constructs in JADE. The advantages of using a goal-oriented early requirements analysis and its influence on subsequent phases of analysis and design are also presented. Finally, we also propose UML 2.0 diagrams at several different levels for representation of GAIA deliverables, like organizational structure, role and interaction model, agent and service model.

What characterizes the existence of a person? The fact that it has a blood and flesh body, the actions and goals achieved during is life, or both? Like we all have a real existence, we propose to create a virtual existence for each one of us. Like our real life, our virtual life would have knowledge of our goals and desires in all areas. Like our real life, our virtual life would have autonomy. It would know all about us, our ID number and data, our IRS information, our health information, and so on.

This is the advanced prototype I have developed during my PhD work to prove my hypotheses. It is a Distributed and Autonomous System that represents the Airline Operational Control Center with capacity to provided an Integrated solution to the problems in real-time and with adaptive characteristics. More information in http://www.disruptionmanagement.com