| Course Code and Name: SEEM3570 Stochastic Models | ||||||||||||||
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| Course Objectives: - Basics of stochastic linear programming - Stochastic processes and Markov chains - Birth-and-death processes and queuing models. Stochastic inventory models: single and multiple periods. - Markov decision processes. |
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| Course Outcomes: - Ability to critically evaluate the importance of uncertainty in decision problems. Includes the ability to question any statements based on deterministic modeling and thinking. - A certain ability to pick the right tool for stochastic decision problems. | Programme Outcomes: (P1) The ability to apply knowledge of mathematics, science, and engineering appropriate to the degree discipline (K/S) (P2) The ability to design and conduct experiments, as well as to analyze and interpret data (K/S) (P3) The ability to design a system, component, or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (K/S) (P4) The ability to function in multi-disciplinary teams (S/V) (P5) The ability to identify, formulate, and solve engineering problems (K/S) (P6) The understanding of professional and ethical responsibility (V) (P7) The ability to communicate effectively (S) (P8) The ability to understand the impact of engineering solutions in a global and societal context, especially the importance of health, safety and environmental considerations to both workers and the general public (V) (P9) The ability to recognize the need for, and to engage in life-long learning (V) (P10) The ability to stay abreast of contemporary issues (S/V) (P11) The ability to use the techniques, skills, and modern engineering tools necessary for engineering practice appropriate to the degree discipline (K/S) (P12) The ability to use the computer/IT tools relevant to the discipline along with an understanding of their processes and limitations (K/S/V) (P13) The ability to apply the skills relevant to the discipline of operations research and information technology and their applications in engineering and managerial decision making, especially in financial services, logistics and supply chain management, business information systems, and service engineering and management (K/S) K = Knowledge outcomes S = Skills outcomes V = Values and attitude outcomes |
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| Weights (in %): | ||||||||||||||
| P1 | P2 | P3 | P4 | P5 | P6 | P7 | P8 | P9 | P10 | P11 | P12 | P13 | Other | Total |
| 10% | 15% | 15% | 10% | 10% | 40% | 100% | ||||||||
| Course Outcome(s) is/are measurable or not: Yes If Yes, please suggest ways to measure: (P1): The course is about using mathematics to formulate and solve decision-problems. Can be measured by testing the ability to formulate models. (P2): The course focuses on the relationship between the quality of model results, input data, and the model itself. Can be measured by qualitative questions in exams focusing on what the data and the results actually mean. (P7): There is a focus on communicating stochastic phenomena via mathematics and verbal outlines. Can be measured at for (P1). (P12): The IT tools used are moderate, but there is a serious focus on understanding the relationship between the limitations of many (particularly deterministic) tools and the problem being analyzed. Can be measures as for (P2). (P13): The course is a core OR subject, with important implications for planning in all the listed applications, which are all seriously affected by uncertainty. Can be measured as for (P1) and (P2). |
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