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International audience; In this communication the formulation of optimization problems using fuzzy dual parameters and variables is introduced to cope with parametric or implementation uncertainties. It is shown that fuzzy dual programming problems generate finite sets of deterministic optimization problems, allowing to assess the range of the solutions and of the resulting performance at an acceptable computational effort.

International audience; Airports are asset-intensive businesses that require a large amount of time to recover the significant financial investments in specific infrastructure such as runways and terminals. Airports investors must perform strategic moves based on calculated risks before taking investment decisions. This communication puts forward a new approach for airport investment risk assessment. The approach takes explicitly into account the degree of uncertainty in activity levels prediction and proposes milestones for the different stages of the project for minimizing risk. Uncertainty is represented through fuzzy dual theory and risk management is performed using dynamic programming

International audience; The construction of a new airport or the extension of an existing one requires massive investments and many times public private partnerships were considered in order to make feasible such projects. One characteristic of these projects is uncertainty with respect to financial and environmental impacts on the medium to long term. Another one is the multistage nature of these types of projects. While many airport development projects have been a success, some others have turned into a nightmare for their promoters. This communication puts forward a new approach for airport investment risk assessment. The approach takes explicitly into account the degree of uncertainty in activity levels prediction and proposes milestones for the different stages of the project for minimizing risk. Uncertainty is represented through fuzzy dual theory and risk management is performed using dynamic programming. An illustration of the proposed approach is provided.

The construction of a new airport or the extension of an existing one requires massive investments and many times public private partnerships were considered in order to make feasible such projects. One characteristic of these projects is uncertainty with respect to financial and environmental impacts on the medium to long term. Another one is the multistage nature of these types of projects. While many airport development projects have been a success, some others have turned into a nightmare for their promoters. This communication puts forward a new approach for airport investment risk assessment. The approach takes explicitly into account the degree of uncertainty in activity levels prediction and proposes milestones for the different stages of the project for minimizing risk. Uncertainty is represented through fuzzy dual theory and risk management is performed using dynamic programming. An illustration of the proposed approach is provided., {"references":["International Air Transport Association - IATA, \"Fact sheet: Economic\nand social benefits of air transport\" (Web page), accessed July 2014.","M. Streck, M. Rossbach, Discontinuous innovation, (Book style), 2013,\npp. 419-422","T. Donnet, R. Keast, Fitting airport privatization to purpose: aligning\ngovernance, time and management focus, Issue 11(2), 2011, pp.98-114,\nwww.ejtir.tbm.tudelft.nl.","International Air Transport Association - IATA, B. Pearce, \"IATA\nEconomics Briefing Nº10 – Profitability and the air transport value\nchain, June 2013, v 1.1. www.iata.org.","M. Tretheway, K. Markhvida, Airports in the aviation value chain:\nfinancing, returns, risk and investment, Discussion paper 2013-15,\nInternational Transport Forum, May 2013, Germany.","Airports Council International, 2011 Annual report, pp. 12,\nwww.aci.aero.","N.N. Taleb, The Black Swan: The impact of highly improbable (Book\nstyle), 2007, 1st ed., Random House, New York, USA.","C.A.N. Cosenza, O. Lengerke, F. Mora Camino, \"Fuzzy sets and dual\nnumbers:an integrated framework\", (Published Conference Proceedings\nstyle) 9th Fuzzy Sets and Knowledge Discovery Conference, Chonqing,\nChina, 2012.","S.F. Hillier, J.G. Lieberman, Introduction to operations research, p.424,\n9th ed., McGraw Hill International Edition, Singapore, 2010.\n[10] C.A.N Cosenza, F. Mora Camino, \"Programming with fuzzy dual\nuncertainty\", (Published Conference Proceedings style) CLAIO –\nCongreso Latino-Ibero-Americano de Investigacion Oeprtiva, Rio de\nJaneiro, Brazil, 2012."]}

International audience; In this communication the ground handling fleet management problem at airports is considered with the aim of improving aircraft service at arrival and departure while the operational cost of the ground service fleets is taken into account. The complexity of the co nsidered problem, as well as, operational considerations lead to propose an on-line decentralized management structure where the criticality of each aircraft demand for service is evaluated using a fuzzy formalism. Then after detailing the proposed collaborative scheme between ground handling fleet managers, airlines and airport authorities, a heuristic approach is proposed to solve each fleet assignment problem. A case study considering a large airport is discussed.

International audience

The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the Coronary Heart Disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts- knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

International audience; Nowadays, and with the current progress in technologies and business sales, databases with large amount of data exist especially in retail companies. The main objective of this study is to reduce the complexity of the classification problems while maintaining the prediction classification quality. We propose to apply the promising technique Rough Set theory which is a new mathematical approach to data analysis based on classification of objects of interest into similarity classes, which are indiscernible with respect to some features. Since some features are of high interest, this leads to the fundamental concept of "Attribute Reduction". The goal of Rough set is to enumerate good attribute subsets that have high dependence, discriminating index and significance. The naïve way of is to generate all possible subsets of attribute but in high dimension cases, this approach is very inefficient while it will require 2d -1 iterations. Therefore, we apply Dynamic programming technique in order to enumerate dynamically the optimal subsets of the reduced attributes of high interest by reducing the degree of complexity. Implementation has been developed, applied, and tested over a 3 years historical business data in Retail Business. Simulations and visual analysis are shown and discussed in order to validate the accuracy of the proposed tool.

International audience; Nowadays, and with the current progress in technologies and business sales, databases with large amount of data exist especially in Retail Companies. The main objective of this study is to reduce the complexity of the classification problems while maintaining the prediction classification quality. We propose to apply the promising technique Rough set theory which is a new mathematical approach to data analysis based on classification of objects of interest into similarity classes, which are indiscernible with respect to some features. Since some features are of high interest, this leads to the fundamental concept of "Attribute Reduction". The goal of Rough set is to enumerate good attribute subsets that have high dependence, discriminating index and significance. The naïve way of is to generate all possible subsets of attribute but in high dimension cases, this approach is very inefficient while it will require 2d - 1 iterations. Therefore, we propose the Dynamic programming technique in order to enumerate dynamically the optimal subsets of the reduced attributes of high interest by reducing the degree of complexity. Implementation has been developed, applied, and tested over a 3 years historical business data in Retail Business (RB). Simulations and visual analysis are shown and discussed in order to validate the accuracy of the proposed tool.

International audience