Classical energy planning models assume that consumers are rational, which is obviously rarely the case. This paper proposes an original method to take into account the consumer's real behavior in an energy model. This new hybrid model combines technical methods from operations research with behavioral approaches from social sciences and couples a classical energy model with a Share of Choice model

Operational risks are defined as risks of human origin. Unlike financial risks that can be handled in a financial manner (e.g. insurances, savings, derivatives), the treatment of operational risks calls for a "managerial approach". Consequently, we propose a new way of dealing with operational risk, which relies on the well known aggregate planning model.To illustrate this idea, we have adapted this model to the case of a back office of a bank specializing in the trading of derivative products. Our contribution corresponds to several improvements applied to stochastic programming techniques. First, the model is transformed into a multistage stochastic program in order to take into account the randomness associated with the volume of transaction demand and with the capacity of work provided by qualified and non-qualified employees over the planning horizon. Second, as advocated by Basel II, we calculate the probability distribution based on a Bayesian Network to circumvent the difficulty of obtaining data which characterizes uncertainty in operations. Third, we go a step further by relaxing the traditional assumption in stochastic programming that imposes a strict independence between the decision variables and the random elements. Comparative results show that in general these improved stochastic programming models tend to allocate more human expertise in order to hedge operational risks. Finally, we employ the dual solutions of the stochastic programs to detect periods and nodes that are at risk in terms of the expertise availability.

La plupart des modèles de pricing appliqués aux services sont consacrés aux prestations de services tels que vols en avion ou chambres d'hôtel. Ces techniques peuvent être utilisées à de telles prestations étant donné leur caractère standardisé. Comparés aux biens de production, les services sont caractérisés par les 4 dimensions IHIP. Ainsi, l'intangibilité et l'hétérogénéité des services impliquent qu'il n'est pas aisé d'appliquer des systèmes de prix automatisés. Dans nos récents travaux de recherche, nous avons exploré différents modèles de pricing qui pourraient être utilisés pour déterminer un prix "juste" prenant en compte le point de vue des fournisseurs et des consommateurs de ces services. Pour répondre à cette problématique, nous avons adopté une approche multidisciplinaire essentiellement basée sur les sciences environnementales.

A new concept is proposed for linking algebraic modeling languages with structure-exploiting solvers. SPI (Structure-Passing Interface) is a program that retrieves structure from an anonymous mathematical program built by an algebraic modeling language. SPI passes the special structure of the problem to an SES (Structure-Exploiting Solver). An integration of SPI and SES leads to SET (Structure-Exploiting Tool) and can be used with any algebraic modeling language. This approach relies on the idea that most exploitable block structures can be easily detected from the algebraic formulation of models. It should enable algebraic modeling languages to access the large body of algorithmic techniques which require problem structure.algebraic modeling language, large scale optimization, structure-exploiting solver