Our research in machine learning applications is driven by the belief that data-driven decision-making is the key to achieving operational excellence.  We seek to harness the transformative potential of these techniques to reshape how organizations optimize their processes. For instance, by employing k-means clustering, we unearth hidden patterns in data, allowing for optimized resource allocation and demand forecasting. Neural networks enable us to build predictive models that adapt and learn from evolving operational data, providing invaluable insights for decision-makers. Additionally, decision trees empower organizations to make complex choices by mapping out decision pathways based on data-driven criteria. The main goal is to provide computational intelligence support to complex, data-oriented, operations performance management problems.

In the realm of operations management, Natural Language Processing (NLP) and text analytics techniques can unlock profound insights from unstructured text data. Our expertise spans various applications, including Aspect-Based Sentiment Analysis for mapping customer value perception, Social Media Mining for real-time market insights, and Topic Modeling to assess the discourse of key stakeholders. With Aspect-Based Sentiment Analysis, we dissect customer feedback to precisely understand their perceptions of different product or service aspects, enabling organizations to fine-tune their offerings accordingly. Our Social Media Mining capabilities have the ability to monitor and respond to trends, sentiments, and emerging issues in real-time. Additionally, through Topic Modeling, we dissect textual data to identify prevalent themes and concerns among stakeholders. These powerful NLP and text analytics techniques empower organizations to make data-driven decisions, enhance customer satisfaction, and proactively address operational challenges in today's complex business landscape.

Soft computing deals with approximate models and gives solutions to complex real-life problems. Unlike hard computing, soft computing is tolerant of imprecision, partial truth, and approximations. In effect, the role model for soft computing is the human mind. Soft computing is based on techniques such as fuzzy logic and grey systems theory. Performance management is a process of linked activities that aim to ensure goals are being met in the most efficient and productive way possible. Within organisations, performance management attempts to drive efficiency of operations by aligning internal and external activities with the company’s objectives. Therefore, this research subject encompasses the application of soft computing techniques to enhance, promote, assess and support operations performance management in cases in which it would not be possible, such as when there is presence of hesitation, uncertainty, small and incomplete data sets.

The decision-making process is often a complex activity as it involves consideration of several objectives and alternatives. Applying MCDM methods is recommended in these situations for they are able to handle the need of satisfying multiple objectives and analyzing different alternatives when deciding. In organizational context, where most of the decisions need to contemplate different interests from areas or departments involved, applying MCDM methods enable managers with adequate support to their decision-maker activities and offer more consistent answers to specific organizational demands. These methods encompass different problematic and may be applied, for instance, to select or rank alternatives of action accordingly to some criteria, or to classify alternatives into pre-defined categories.