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
Machine learning techniques have proven good performance in classification matters of all kinds: medical diagnosis, character recognition, credit default and fraud prediction, and also foreign exchange market prognosis. Customer segmentation in private banking sector is an important step for profitable business development, enabling financial institutions to address their products and services to homogeneous classes of customers. This paper approaches two of the most popular machine learning techniques, Neural Networks and Support Vector Machines, and describes how each of these perform in a segmentation process.
Businesses and technology play an important role in the global economy, where the achievement of sustainability goals has a positive impact on society and companies. In this regard, there is a need to integrate information technology and sustainability to enable companies to act in a greener manner. Knowing that the Enterprise Resource Planning (ERP) system is one of the most powerful business solutions for companies, it is crucial to align its use with sustainability elements through the use of green information technology (IT). We explored the relationship between ERP systems and green IT practices to assess how green IT provides an opportunity for organizations to improve ERP systems in more environmentally responsible initiatives. For that purpose, we empirically analyzed four Romanian economic sectors based on an online survey that contained criteria to explore organizational awareness about developing green ERP systems. The findings indicate that companies are generally focused on the key benefits of ERP systems related to operational aspects and less on the sustainability benefits. Based on these results, the main conclusion highlights the strong need to embed IT in business sustainability initiatives by adopting green IT solutions.
This paper presents a software agent based framework's architecture for boosting performance in supply chain management applications. The framework is based on agent interaction and semantic web service composition. The purpose of such a platform is to develop flexible business applications for SCM transactions modeling, in collaborative and distributed economic systems. The interaction between agents is limited by a cybernetic model that takes into account several constraints one of the main being bankruptcy risk potential of the peer partner company.
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