HDRs 2023

Nowadays, the manufacturing industry operates in a much more complex and volatile environment than in past decades. To meet this challenge, manufacturers need advanced software tools capable of designing and managing manufacturing systems and supply chains to make them robust, resilient, flexible, and reconfigurable. In this context, my work focuses on two key issues: assembly line design and batch sizing. My recent contributions aim to enhance decision models for making robust and resilient decisions. Robustness is the ability to perform well under different conditions, while resilience is the ability to adapt and recover. I work with three families of optimization approaches under uncertainty, namely stochastic programming, robust optimization, and constrained Markov decision process resolution methods. To provide robust decisions, we enhance the assembly line design and batch planning model to account for various uncertainties. To make the proposed solution resilient, we incorporate dynamic decision-making. The resulting optimization model takes into account corrective actions available for each scenario and suggests solutions where the system adapts effectively to a wide range of situations.

The work presented in this "Habilitation à Diriger des Recherches" (Accreditation to Supervise Research) focuses on the analysis and indexing of scientific texts and lies at the intersection of two research themes: Natural Language Processing (NLP), which involves the analysis, understanding, and generation of natural language, and Information Retrieval (IR), which studies ways to retrieve information from a collection of documents. We are interested in the question of scholarly document retrieval, which involves searching for documents in the scientific literature (e.g., articles, books, theses) related to a specific subject of study. More specifically, our research aims to enhance the metadata associated with documents to improve their accessibility and dissemination. Our work focuses on the development of automated methods for keyword generation, which are characterized by the unique utilization of graph-based techniques and node ranking algorithms. We delve into the issue of indirectly evaluating automatically generated keywords through application-specific tasks and their utilization in search engines and academic recommendation systems. We present our efforts into constructing linguistic resources, developing software tools, and their dissemination within the scientific community. Finally, we conclude with some prospective insights into keyword indexing and, more broadly, the emerging research at the intersection of NLP and IR themes.