Le nouveau canal du Centre est-il un projet rentable ?

The « canal du Centre » upgrading to 1350 tons will cost 35 billions B.F. This paper analyses the economic costs and benefits of the project on the basis of a traffic forecast and the computation of operating and transportation costs of the various alternatives: keeping the old canal, upgrading and closing the canal. À sensitivity analysis of the results is performed. The conclusions are not favorable to the upgrading solution.La modernisation à 1 350 tonnes du canal du Centre aura coûté 36 milliards de F.B. Cette étude calcule les coûts et bénéfices économiques de ce projet sur base d'une prévision du trafic futur ainsi que des coûts de fonctionnement et de transport associés aux diverses alternatives possibles : maintien de l'ancien canal, modernisation et fermeture du canal. Les calculs sont l'objet d'une analyse de sensibilité. Les conclusions ne sont pas favorables à la solution de modernisation

Solving surgical cases assignment problem by a branch-and-price approach

In this paper, we study a surgical cases assignment problem (SCAP) of assigning a set of surgical cases to several multifunctional operating rooms with an objective of minimizing total operating cost. Firstly, we formulate this problem as an integer problem and then reformulate the integer program by using Dantzig-Wolf decomposition as a set partitioning problem. Based on this set partitioning formulation, a so-called branch-and-price exact solution algorithm, combining Branch-and-Bound procedure with column generation (CG) method, is designed for the proposed problem where each node is the linear relaxation problem of a set partitioning problem. This linear relaxation problem is solved by a CG approach in which each column represents a plan for one operating room and is generated by solving a sub-problem (SP) of single operating room planning problem. The computational results indicate that the decomposition approach is promising and capable of solving large problems.

Predicting Academic Performance by Data Mining Methods

Academic failure among first-year university students has long fuelled a large number of debates. Many educational psychologists have tried to understand and then explain it. Many statisticians have tried to foresee it. Our research aims to classify, as early in the academic year as possible, students into three groups: the 'low-risk' students, who have a high probability of succeeding; the 'medium-risk' students, who may succeed thanks to the measures taken by the university; and the 'high-risk' students, who have a high probability of failing (or dropping out). This article describes our methodology and provides the most significant variables correlated to academic success among all the questions asked to 533 first-year university students during November of academic year 2003/04. Finally, it presents the results of the application of discriminant analysis, neural networks, random forests and decision trees aimed at predicting those students' academic success.Academic performance, decision trees, random forests, neural networks, discriminant analysis, education, prediction,

Using a KDD process to forecast the duration of surgery

This paper presents a methodological framework for planning surgery in operating theatre suites based on data warehousing and knowledge discovery in database approaches. We suggest a decisional tool which estimates the appropriate duration for a patient to be in the operating theatre. To achieve this, we first describe a data warehouse model used to extract data from various, possibly non-interacting, databases. Then we compare two data mining methods: rough sets and neural networks. The aim is to identify classes of surgery likely to take different lengths of time according to the patient's profile. These tools permit patients' profiles to be identified from administrative data, previous medical history, etc. The surgical environment (surgeon, type of anesthesia, etc.) is also taken into account in estimating the duration of the surgery.

Multicriteria approach to rank scheduling strategies

This paper deals with multicriteria decision-making applied to discrete-continuous scheduling problems. The need for reusability and modularity leads us to build a "generic" optimization and simulation framework, while the need to quickly generate good compromises between conflicting objectives requires the implementation of multicriteria scheduling models. This paper describes the practical possibilities of two hybrid models within this framework, the first one uses a lexicographical sort and the second one a multicriteria method to rank scheduling strategies. The two hybrids are applied to a real-life highly constrained industrial problem.

A Multi-objective Hospital Operating Room Planning and Scheduling Problem Using Compromise Programming

15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancún, Mexico, October 23–28, 2016, Proceedings, Part IInternational audienceThis paper proposes a hybrid compromise programming local search approach with two main characteristics: a capacity to generate non-dominated solutions and the ability to interact with the decision maker. Compromise programming is an approach where it is not necessary to determine the entire set of Pareto-optimal solutions but only some of them. These solutions are called compromise solutions and represent a good tradeoff between conflicting objectives. Another advantage of this type of method is that it allows the inclusion of the decision maker’s preferences through the definition of weights included in the different metrics used by the method. This approach is tested on an operating room planning process. This process incorporates the operating rooms and the nurse planning simultaneously. Three different objectives were considered: to minimize operating room costs, to minimize the maximum number of nurses needed to participate in surgeries and to minimize the number of open operating rooms. The results show that it is a powerful decision tool that enables the decision makers to apply compromise alongside optimal solutions during an operating room planning process