Chromolaena odorata (Siam weed) has been classified as a weed plant in West Africa. Data from C. odorata foliage after 4, 6, 8, 10 and 12 weeks of regrowth showed that the leaf fraction had a crude protein content above 194 g kg–1 dry matter (DM) and an average leaf to stem ratio of 2·1:1. Chemical analysis of the leaf fraction of an 8‐week‐old regrowth indicated a high crude protein content (258 g kg–1 DM) and a high degradable nitrogen content (60·7 g N kg–1 digestible organic matter), but low neutral‐detergent fibre (331 g kg–1 DM), acid‐detergent lignin (53·1 g kg–1 DM), total extractable phenolic (37·1 g kg–1 DM), extractable tannin (0·72 absorbance at 550 nm) and extractable condensed tannin (1·4 g kg–1 DM) contents. In sacco degradability analysis of the 8‐week‐old regrowth leaf sample showed a high 48 h organic matter (935 g kg–1 DM) and crude protein (953 g kg–1 DM) degradability. The leaf sample had an organic matter degradability of 670 g kg–1 DM as estimated by cumulative gas production in vitro after 24 h incubation. There was little or no phenolic‐related antinutritive factors in C. odorata. Additionally, leaf samples had no effect on rumen protozoa activity estimated as the rate of [14C]leucine Selenomonas ruminantum bacterial protein breakdown. Data from this study suggest that C. odorata leaves are of high nutritive value and might have the potential to be used as a protein supplement to ruminants. There is need for further investigation to test whether C. odorata leaves may have any deleterious effect on the host animal.

Abstract

Most of the research efforts dealing with airline scheduling have been done on off-line plan optimization. However, nowadays, with the increasingly complex and huge traffic at airports, the real challenge is how to react to unexpected events that may cause plandisruptions, leading to flight delays.Moreover these disruptive events usually affect at least three different dimensions of the situation: the aircraft assigned to the flight, the crew assignment and, often forgotten, the passengers' journey and satisfaction.This book includes answers to this challenge and proposes the use of the Multiagent System paradigm to rapidly compose a multi-faceted solution to the disruptive event taking into consideration possible preferences of those three key aspects of the problem.Negotiation protocols taking place between agents that are experts in solving the different problem dimensions, combination of different utility functions and, not less important, the inclusion of the human in the automatic decision-making loop make MASDIMA, the system described in this book, well suited for real-life plan-disruption management applications. Engineering

No 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.

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.

Most of the research efforts dealing with airline scheduling have been done on off-line plan optimization. However, nowadays, with the increasingly complex and huge traffic at airports, the real challenge is how to react to unexpected events that may cause plandisruptions, leading to flight delays.Moreover these disruptive events usually affect at least three different dimensions of the situation: the aircraft assigned to the flight, the crew assignment and, often forgotten, the passengers' journey and satisfaction.This book includes answers to this challenge and proposes the use of the Multiagent System paradigm to rapidly compose a multi-faceted solution to the disruptive event taking into consideration possible preferences of those three key aspects of the problem.Negotiation protocols taking place between agents that are experts in solving the different problem dimensions, combination of different utility functions and, not less important, the inclusion of the human in the automatic decision-making loop make MASDIMA, the system described in this book, well suited for real-life plan-disruption management applications. Engineering

Abstract: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. Keywords: multi-agent systems; agent-oriented methodologies; goal-oriented requirements analysis; operations recovery; disruption management.Reference to this paper should be made as follows: Castro, A. and Oliveira, E. (2008) 'The rationale behind the development of an airline operations control centre using Gaia-based methodology', Int. J. Vol. 2, No. 3, Biographical notes: António Castro is a Software Engineer at TAP Portugal, the Portuguese airline company, where he manages software projects related to crew scheduling and operations control. He is currently a PhD student of Informatics Engineering at the Faculty of Engineering, University of Porto. His current research interests include multi-agent systems, agent-oriented software engineering and distributed systems. The rationale behind the development of an airline OCC 351Eugénio Oliveira is a Full Professor at the University of Porto, Portugal. He coordinates a research group on distributed artificial intelligence in the LIACC Lab and a doctoral programme in informatics. He obtained his PhD in Knowledge Engineering/Logic Programming from the Universidade Nova in Lisbon (1984). He was Guest Academic at IBM/IEC, Belgium (1984Belgium ( -1985. He was awarded the Gulbenkian Prize in 1983. He has participated in European and nationally funded projects involving intelligent agents. Agent-based frameworks for B2B, multi-agent learning and agent-based teamwork are current topics of interest. He has published a number of papers in journals and proceedings.