António J. M. Castro

The rationale behind the development of an airline operations control centre using Gaia-based methodology

In this paper, we report how we complemented Gaia methodology to analyse and design a multi-agent system for an airline company operations control centre. 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 the representation of Gaia deliverables, such as organisational structure, role and interaction model, and agent and service model.

Using Specialized Agents in a Distributed MAS to Solve Airline Operations Problems: A Case Study

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. This MAS has several specialized software agents that implement different algorithms, competing to find the best solution for each problem. We present a real case study where a crew recovery problem is solved. We show that it is possible to find valid solutions, in less time and with a smaller cost.

A New Concept for Disruption Management in Airline Operations Control

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.

A new approach for disruption management in airline operations control

As a sequel of a recent Ph.D. thesis at the University of Porto and LIACC research lab, a new book appeared at Springer, Series: Studies in Computational Intelligence, Vol. 562, 2014, XII, 242 p., entitled A New Approach for Disruption Management in Airline Operations Control by Antonio J. M. Castro, Ana Paula Rocha, and Eugénio Oliveira.

Towards an autonomous and intelligent Airline Operations Control

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.

Disruption Management in Airline Operations Control – An Intelligent Agent-Based Approach

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. It is organized as follows: In Section 2 we present some related regarding operations recovery, a classification of current tools and systems in use in some airline companies and a brief summary of the current use of software agents’ technology in other domains. Section 3 introduces the Airline Operations Control Centre (AOCC), including typical organizations and problems, the current disruption management (DM) process and a description of the main costs involved. Section 4 is the main section of this chapter and presents our agent-based approach to this problem. This section presents: (i) the reasons that made us adopt the software agents and multi-agent system (MAS) paradigm; (ii) the MAS architecture including the specific agents, roles and protocols as well as some relevant agent characteristics like autonomy and social-awareness; (iii) decision 6

Recovering from Airline Operational Problems with a Multi-Agent System: A Case Study

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.

Operational problems recovery in airlines: a specialized methodologies approach

Disruption management is one of the most important scheduling problems in the airline industry because of the elevated costs associated, however this is relatively new research area comparing for example with fleet and tail assignment. The major goal to solve this kind of problem is to achieve a feasible solution for the airline company minimizing the several costs involved and within time constraints. An approach to solve operational problems causing disruptions is presented using different specialized methodologies for the problems with aircrafts and crewmembers including flight graph based with meta-heuristic optimization algorithms. These approaches were built to fit on a multi-agent system with specialist agents solving disruptions. A comparative analysis of the algorithms is also presented. Using a complete month real dataset we demonstrate an example how the system handled disruption events. The resulting application is able to solve disruption events optimizing costs and respecting operational constraints.

A Multi-Agent System for Airline Operations Control

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 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 and 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.

Artificial Bee Colony Algorithm for Solving the Flight Disruption Problem.

This paper presents the optimization algorithm Artificial Bee Colony (ABC) firstly introduced by in 2005 and proposed for optimizing numerical problems. ABC is the swarm-based meta-heuristic algorithm inspired by intelligent behavior of honey bee colonies. In this paper, ABC has been applied on solving the flight disruption problem, by swapping aircraft and/or cancelling/delaying flights, and its performance has been shown through experimentation. The environment and data for experiments are provided by MASDIMA, Multi-Agent System for DIsruption MAnagement developed by LIACC (Laboratory of Artificial Intelligence and Computer Science).