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Abstract: This paper focuses on the application of the analytical hierarchy process (AHP) technique in the context of production and distribution of anti cancer drugs within the pharmacy department of a French hospital. This is achieved by evaluating how AHP can support the drug production planning process which aims at minimizing the cost associated with the drug preparation process while satisfying patients. The approach proposed has been applied to a case study which is the pharmaceutical chemotherapy compounding unit of the Georges Pompidou European Hospital (HEGP, AP-HP, Paris). Quantitative weightings from the AHP model are used to identify drugs that the pharmacy can produce in advance, i.e. on a MTS (make to stock) basis. Because of its ease of implementation and results it enabled to reach, the approach developed at Georges Pompidou has been extended to other pharmacies in France. More generally, the work carried out in this paper is an example of illustration of how AHP can be used (with its strengths and weaknesses) as a decision-support tool in health care management. [Copyright &y& Elsevier]

Abstract: This paper presents a decision support system devoted to the selection of films for the International Animated Film Festival organized at Annecy, France, every year. It deals with the representation and aggregation of referees’ preferences along predefined criteria in addition to their overall selection point of view. The practical requirements associated with this application (often encountered in social or cultural areas as well) are: a common ordinal scale for the criteria scores, a procedure to deal with inconsistencies between criteria and overall scores, explanation tools of each referee’s preference model in order to facilitate the deliberation process and also to argument the selection decision. The processing of referees’ preferences is achieved thanks to a recent method which consists in finding a generalized mean aggregation operator representing the preferences of a referee, in a finite ordinal scale context. The method allows to deal with consistency conditions on referees’ behaviour in order to highlight the criteria or pair of criteria which are the most influential for each of the referees. All the functionalities have been implemented in an interactive decision software that facilitates a shared selection decision. Results issued from the 2007 selection are presented and analysed from the preference representation and processing point of view. [Copyright &y& Elsevier]

The COVID-19 virus outbreak has affected most of the world in 2020. This paper deals with artificial intelligence (AI) methods that can address the problem of predicting scale, dynamics and sensitivity of the outbreak to preventive actions undertaken with a view to combatting the epidemic. In our study, we developed a cellular automata (CA) model for simulating the COVID-19 disease spreading. The enhanced infectious disease dynamics S E I R (Susceptible, Exposed, Infectious, and Recovered) model was applied to estimate the epidemic trends in Poland, France, and Spain. We introduced new parameters into the simulation framework which reflect the statistically confirmed dependencies such as age-dependent death probability, a different definition of the contact rate and enhanced parameters reflecting population mobility. To estimate key epidemiological measures and to predict possible dynamics of the disease, we juxtaposed crucial CA framework parameters to the reported COVID-19 values, e.g. length of infection, mortality rates and the reproduction number. Moreover, we used real population density and age structures of the studied epidemic populations. The model presented allows for the examination of the effectiveness of preventive actions and their impact on the spreading rate and the duration of the disease. It also shows the influence of structure and behavior of the populations studied on key epidemic parameters, such as mortality and infection rates. Although our results are critically dependent on the assumptions underpinning our model and there is considerable uncertainty associated with the outbreaks at such an early epidemic stage, the obtained simulation results seem to be in general agreement with the observed behavior of the real COVID-19 disease, and our numerical framework can be effectively used to analyze the dynamics and efficacy of epidemic containment methods. • Cellular Automata include density population of Poland, France, Spain in SEIR model. • We analyze influence of parametric adjustment for modeling pandemic prevention. • The earlier reduction of personal contacts the faster reduction of infections number. • Age dependent mortality rates are explored to reflect the real features of COVID-19. • Our approach allows for modeling of COVID-19 presumable dynamics. [ABSTRACT FROM AUTHOR]