Maximisation du profit

Résumé

En économie, la maximisation du profit est le processus par lequel une entreprise détermine le niveau des prix et de sortie qui génère le plus grand profit. Il existe plusieurs approches à ce problème. La méthode du coût total des recettes totales se fonde sur le fait que le profit est égal au coût des recettes en moins, et la méthode du coût marginal de recettes marginales est basée sur le fait que le profit total dans un marché parfaitement concurrentiel atteint son point maximum où le revenu marginal est égal au coût marginal. Définitions de base Les frais engagés par une entreprise peuvent être classés en deux groupes : les coûts fixes et coûts variables. Les coûts fixes sont engagés par l'entreprise à tout niveau de production, y compris une production nulle. Il peut s'agir d'entretien de l'équipement, le loyer, les salaires et l'entretien général. Les coûts variables changent avec le niveau de production, augmente à mesure que plus de produit est généré. Matériaux

Source officielle

https://fr.wikipedia.org/wiki/Maximisation_du_profit

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Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsupervised learning problem. Current machine learning methods process new sensory data using prior knowledge defined by either domain knowledge or datasets. When datasets are not available, data acquisition is needed, though automating exploration in support of learning is still an unsolved problem. Here we develop a method that enables agents to efficiently collect data for learning a predictive sensor model-without requiring domain knowledge, human input, or previously existing data-using ergodicity to specify the data acquisition process. This approach is based entirely on data-driven sensor characteristics rather than predefined knowledge of the sensor model and its physical characteristics. We learn higher quality models with lower energy expenditure during exploration for data acquisition compared to competing approaches, including both random sampling and information maximization. In addition to applications in autonomy, our approach provides a potential model of how animals use their motor control to develop high quality models of their sensors (sight, sound, touch) before having knowledge of their sensor capabilities or their surrounding environment. Information-based search strategies are relevant for the learning of interacting agents dynamics and usually need predefined data. The authors propose a method to collect data for learning a predictive sensor model, without requiring domain knowledge, human input, or previously existing data.

NATURE PORTFOLIO2022

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Optimization of the Distance between Cylindrical Light Distributors Used for Interstitial Light Delivery in Biological Tissues

Aurelien Stephen Arnaud Gregor, Georges Wagnières

Cylindrical light diffusers (CLDs) are often employed for the treatment of large tumors by interstitial photodynamic therapy (iPDT) and photoimmunotherapy (PIT), which involves careful treatment planning to maximize therapeutic dose coverage while minimizing the number of CLDs used. There is, however, a lack of general guidelines regarding optimal positioning of CLDs, in particular when they are inserted in parallel to treat head and neck squamous cell cancer (HNSCC). Therefore, the purpose of this study is to determine the CLD-CLD distances maximizing the necrosis for different geometries of CLD positions and shed light on the influence of different optical parameters on this distance, in particular when HNSCCs are treated by interstitial PIT with cetuximab-IR700 using up to seven CLDs. To that end, Monte-Carlo simulations of the light propagation around CLDs inserted perpendicularly in a semi-infinite tumor were performed to determine the volume receiving a fluence larger than a therapeutic threshold. An optimization algorithm was then used to calculate and maximize the necrosed tumor volumes. Tumor optical properties were derived from published data. Our findings suggest that optimal CLD positioning maximizing the volume of necrosed tumor during interstitial PIT for typical HNSCC optical properties corresponds to a CLD-CLD distance between 11.5- and 13-mm. Variations of the absorption and reduced scattering coefficients have the greatest influence on CLD placements, while tissue anisotropy factor, CLD insertion geometry, CLD length, and the angular dependence of the radiance emitted by the CLDs have minimal influence. At first approximation the influence of these optical parameters on optimal CLD-CLD distance are independent. Our data also suggests it is possible to derive new treatment plans using knowledge of previous treatment plans.

MDPI2022

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This article presents a model within the sustainable machining paradigm. In general, the optimization of machining process for the minimum energy consumption alone does not have sufficient impetus for application in the manufacturing companies wherein profit maximization remains the preferred mode of operation. In this study, a novel objective function for the optimization of machining process is modelled which determines the optimal machining parameters for the maximum profit per unit energy consumption. The model is applied to an example problem of a single-pass turning operation and is found unique from the past models focused on the minimization of either energy consumed or cost incurred.

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Optimization of the Distance between Cylindrical Light Distributors Used for Interstitial Light Delivery in Biological Tissues

Aurelien Stephen Arnaud Gregor, Georges Wagnières

MDPI2022