Context-based Reasoning (CxBR) and Contextual Graphs (CxGs) involve the modeling of human behavior in autonomous and decision-support situations in which optimal human decision-making is of utmost importance. Both formalisms use the notion of contexts to allow the implementation of intelligent agents equipped with a context sensitive knowledge base. However, CxBR uses a set of discrete contexts, implying that models created using CxBR operate within one context at a given time interval. CxGs use a continuous context-based representation for a given problem-solving scenario for decision-support processes. Both formalisms use contexts dynamically by continuously changing between necessary contexts as needed in appropriate instances. This thesis identifies a synergy between these two formalisms by looking into their similarities and differences. It became clear during the research that each paradigm was designed with a very specific family of problems in mind. Thus, CXBR best implements models of autonomous agents in environment, while CxGs is best implemented in a decision support setting that requires the development of decision-making procedures. Cross applications were implemented on each and the results are discussed.

This thesis presents a comprehensive investigation into the performance and generalizability of optimization approaches for the single-echelon (r, Q) inventory management policy under stochastic demand, specifically focusing on demand characterized by a Poisson distribution. The research integrates both classical optimization techniques and advanced metaheuristic methods, with a particular emphasis on Genetic Programming (GP), to assess the effectiveness of various heuristics. The study systematically compares the performance of these approaches in terms of both accuracy and computational efficiency using two well-known datasets. To rigorously evaluate the generalizability of the heuristics, an extensive random dataset of 10,000 instances, drawn from a vast population of approximately 24 billion instances, was generated and employed in this study. Our findings reveal that the exact solution provided by the Federgruen-Zheng Algorithm consistently outperforms hybrid heuristics in terms of computational efficiency, confirming its reliability in smaller datasets where precise solutions are critical. Additionally, the extended Cooperative Coevolutionary Genetic Programming (eCCGP) heuristic proposed by Lopes et al. emerges as the most efficient in terms of runtime, achieving a remarkable balance between speed and accuracy, with an optimality error gap of only 1%. This performance makes the eCCGP heuristic particularly suitable for real-time inventory management systems, especially in scenarios involving large datasets where computational speed is paramount. The implications of this study are significant for both theoretical research and practical applications, suggesting that while exact solution, i.e., the Federgruen-Zheng Algorithm is ideal for smaller datasets, the eCCGP heuristic provides a scalable and efficient alternative for larger, more complex datasets without substantial sacrifices in accuracy. These insights contribute to the ongoing development of more effective inventory management strategies in environments characterized by stochastic demand. / Thesis / Master of Science (MSc)