Abstract
This paper presents a very special cutting path determination problem appearing in a high precision tools factory, and provides two new heuristics for its resolution. Particular features of both the cutting process, and of the material to be cut, bring in a set of unusual constraints, when compared with other cutting processes, which confer additional complexity and originality to the problem. In particular, this is a matter of practical and economic relevance, since the solution methods are intended to be implemented in a real-life industrial environment. The concept of dynamic graph is exploited to deal with the arc routing problem under study, which is modelled as a dynamic rural postman problem. The constructive heuristics developed, the "higher up vertex heuristic" (HUV) and the "minimum empty path heuristic" (MEP) are tested with real data sets.

Abstract
In this paper we present an extension of the Nelder and Mead simplex algorithm for non-linear programming, which makes it suitable for both unconstrained and constrained optimisation.(1) We then explore several extensions of the method for escaping local optima, which make it a simple, yet powerful tool for optimisation of nonlinear functions with many local optima. A strategy which proved to be extremely robust was random start local search, with a correct, though unusual, setup. Actually, for some of the benchmarks, this simple metaheuristic remained the most effective one. The idea is to use a very large simplex at the begin; the initial movements of this simplex are very large, and therefore act as a kind of filter, which naturally drives the search into good areas. We propose two more mechanisms for escaping local optima, which, still being very simple to implement, provide better results for some difficult problems.

Abstract
This paper presents the results of a qualitative study of public transport users and car users in order to obtain a deeper understanding of travellers' attitudes towards transport and to explore perceptions of public transport service quality. The key findings indicate that in order to increase public transport usage, the service should be designed in a way that accommodates the levels of service required by customers and by doing so, attract potential users. Furthermore, the choice of transport is influenced by several factors, such as individual characteristics and lifestyle, the type of journey, the perceived service performance of each transport mode and situational variables. This suggests the need for segmentation taking into account travel attitudes and behaviours. Policies which aim to influence car usage should be targeted at the market segments that are most motivated to change and willing to reduce frequency of car use.

Abstract
This paper presents a method for modelling and monitoring the individual decision process of wine tasting panelllists. The individual decision of approving or rejecting a wine is based on the panellist's evaluation of the wine characteristics. The decision process is modelled through a logistic regression where the dependent variable is the decision itself (rejected-0 or approved-1) and the independent variables are the characteristics that were considered relevant for the wine evaluation. The process is monitored using a CUSUM control chart for binomial proportions. The control chart variable is the number of disagreements observed (in a sample of size N) between the actual decisions of the panellist and the results anticipated by the logistic regression model. The method was tested in the Port Wine Institute. Such a test revealed that the logistic model is a useful tool for clarifying both the way in which the wine evaluation process is carried out, and how the decisions are taken. Furthermore, the test has also showed that the control chart is responsive to significant changes of the individual decision process.

Abstract
The Intensive Care Unit (ICU) of Hospitals deals with patients in life critical conditions. The Intensive Care Information System (ICS) can therefore provide extremely important information to support medical doctors' (MDs) decisions. For instance, it is critical to manage well information about the evolution of a large amount of infections over time, about the antibiotics administered to each patient, and the impact on his/her life condition. Good quality information and interaction in such an extreme environment is therefore critical for helping MDs target well medicines to patients. This paper describes the initial stages of a project aiming at improving a real ICS, in particular from the interaction point of view, taking into account the stringent usability requirements from the MDs. Through a validated low definition prototype of the infection module of ICS, the paper proposes innovative active ways of providing suggestions to MDs on what actions to take.

Abstract
Markov models have been widely used to represent and analyze user Web navigation data. In previous work, we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable-length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable-length Markov model to summarize user Web navigation sessions up to a given length. Although the summarization ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalize a Web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarization ability.