IE 515 Course Semester Project 2 Guidelines And Requirements

IE 515 Course Semester Project 2 Guidelines and Requirements

The goal of this project is to explore real-world applications of stochastic processes through either an application project involving Markov chain modeling and Markov decision processes or a research paper surveying existing literature in the field of stochastic processes. Students may select from two options: Option A (Application Project) and Option B (Research Paper). The project should be well-structured, thoroughly researched, and demonstrate a clear understanding of the topic, with appropriate use of models, solution approaches, and visualization tools where applicable. The final report must adhere to formatting guidelines, be at least 10 pages long, and be submitted by the deadline.

Paper For Above instruction

Option A: Application Project

Students or teams of up to three students are allowed to select an industrial problem or a problem from published journal literature. The project should describe the problem, develop a Markov decision process or Markov chain model, and propose solution approaches. Emphasis should be placed on the problem statement and the development of the model, including attempting to find an optimal policy and visualizing the results. For industrial applications, the focus is on formulating the problem and the model, with an effort to identify the best solution. When selecting problems from literature, students should formulate the problem as a Markov decision process, consider alternatives, discuss the benefits and pitfalls of each, and visualize results to demonstrate findings.

Option B: Research Paper

This option involves selecting a specific topic within stochastic processes, surveying at least five journal articles to review the existing literature, identifying a research gap, and possibly developing an improved solution or algorithm. This option requires independent work, with an emphasis on critical analysis, synthesis of information, and identifying future research avenues. A review paper exemplifies this approach.

Sources should be credible academic journals such as Interfaces, Expert Systems with Applications, Computers & Industrial Engineering, among others. Conference proceedings and technical reports are discouraged. Students are advised to access resources through the NMSU library database or interlibrary loan service.

Report Format

The project report must be written using a word processor, include figures and tables, and follow these formatting rules:

  • The title page should include the project title, author(s), course title, and date.
  • An abstract (~100 words), table of contents, list of figures, and list of tables should follow the title page.
  • The main body should be a minimum of 10 pages, single-spaced, using Times New Roman 12, with 1-inch margins.
  • All pages (except the title page) should be numbered, and references should be included at the end.
  • The report should contain the following sections:
  • Introduction (including project goals)
  • Statement of the problem
  • Formulation of the Markov chain or decision model
  • Results and discussion
  • Conclusions and future research directions
  • References
  • Appendices, if necessary

The discussion section is critical and should interpret the results in relation to the original goals, compare findings with existing literature, and discuss limitations and potential future work. The conclusion should provide a clear summary of the research's significance and outline future research directions.

Submission and Evaluation

The project report, along with any software developed, must be submitted as a Word file, accompanied by PDFs of references used. Submission should be through the required online system by the deadline (Sunday, May 10, 5 pm US Mountain time). Late submissions will incur a penalty of 10% per day. The project accounts for 50% of the final grade and will be evaluated based on organization, clarity, quality of information, and technical rigor.

For the application project, greater depth is expected for teams of three students. For individual research papers, the maximum score is 9 out of 10 points.

References

  • Puterman, M. L. (1995). Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons.
  • Ross, S. M. (2014). Introduction to Probability Models. Academic Press.
  • Perron, F. (2014). Markov Chains and Stochastic Stability. Springer.
  • Puterman, M. L. (2005). Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons.
  • Bertsekas, D. P. (2005). Dynamic Programming and Optimal Control. Athena Scientific.
  • Howard, R. A. (1960). Dynamic Programming. Elsevier.
  • Altman, E. (1999). Stochastic Networks: The Markov Chain Model. CRC Press.
  • Kallenberg, O. (2002). Foundations of Modern Probability. Springer.
  • Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
  • Puterman, M. L. (1991). Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley.

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