Decision-Making Under Uncertainty
Decision-Making Under Uncertainty (6th semester) develops students’ ability to formalize decision problems in stochastic environments by applying advanced optimization techniques and mathematical modeling tools. Emphasis is placed on representing uncertainty as a structural component of decision systems, enabling the design of robust and efficient solutions under conditions of incomplete or probabilistic information.
The course trains students to (i) diagnose and decompose complex organizational problems, (ii) formulate stochastic models that capture variability and randomness in system behavior, and (iii) apply optimization methods—including simulation and analytical approaches—to evaluate trade-offs and derive optimal or near-optimal policies.
The active learning methodology integrates problem-based learning, case-driven analysis, and reflective discussion, fostering autonomy, critical thinking, and professional accountability. In addition to technical mastery, the course strengthens competencies in strategic communication of results, ensuring students can articulate models, assumptions, and outcomes in complex academic, professional, and organizational contexts.
Operations Research
IN3702 – is a 6th-semester course that equips students with the ability to model and optimize decision-making processes under stochastic uncertainty. The course emphasizes the formulation and solution of discrete and continuous stochastic models, integrating probabilistic structures and temporal dynamics through techniques such as dynamic programming.
Students are trained to abstract complex problem situations into mathematical representations, identify fixed and random components within stochastic systems, and derive optimization models that balance efficiency and robustness. The methodology combines problem-based learning, computational assignments, and case-driven analysis, fostering both technical depth and applied problem-solving skills.
Learning outcomes include: the capability to analyze uncertainty in system interactions, formalize optimization problems with rigorous justification, and implement algorithmic approaches to identify optimal policies. In doing so, students develop a dual competency: (i) technical mastery of stochastic modeling and optimization tools, and (ii) applied judgment to evaluate trade-offs in real-world industrial engineering contexts.
Operations Management II
Operations Management II (IN4704) is an advanced course designed to develop students’ ability to diagnose, model, and resolve complex operational challenges in real organizational settings. The course emphasizes the integration of functional areas—such as marketing, finance, and strategic management—into coherent solutions that enhance overall system efficiency.
Students are trained to (i) analyze capacity constraints by identifying limiting processes and resources in unstructured problem contexts, (ii) evaluate the strategic alignment between business objectives and operational execution under current and projected scenarios, and (iii) design integrated, cross-functional solutions that incorporate organizational goals and supplier networks.
The course applies a systems approach to operations management, using quantitative modeling, scenario analysis, and optimization techniques to generate insights for strategic decision-making. Ethical considerations are embedded throughout, reinforcing the responsibility of aligning operational improvements with sustainable and socially responsible business practices.
Through active, problem-based learning, students gain not only technical expertise in operations modeling and process analysis but also strategic decision-making skills and the ability to articulate solutions across multidisciplinary teams.