STAT479 LINEAR PROGRAMMING
| Course Code: | 2460479 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (2.00 - 2.00) |
| ECTS Credit: | 8.0 |
| Department: | Statistics |
| Language of Instruction: | English |
| Level of Study: | Undergraduate |
| Course Coordinator: | Prof.Dr. ÖZLEM İLK DAĞ |
| Offered Semester: | Fall Semesters. |
Course Objectives
Developing Skills:
1. Problem Formulation and Model Building
- Acquire the skills to effectively formulate and build linear programming models for real-world problems, emphasizing the translation of practical scenarios into mathematical expressions.
2. Analytical and Computational Techniques
- Master the use of the simplex method for solving linear programming problems, including the handling of large-scale linear programs through software applications.
- Develop the ability to perform sensitivity analysis to understand the impact of changes in the parameters on the optimal solution.
Developing Knowledge Base
1. Foundational Principles of Linear Programming
- Gain a thorough understanding of the general principles, underlying assumptions, basic methods, and application areas of linear programming.
2. Special Linear Programming Problems
- Become familiar with the characteristics and solution strategies for special types of linear programming problems such as transportation, transshipment, assignment, and network flow problems.
3. Advanced Theoretical Concepts
- Understand the concepts of duality theory, its relationship with optimality, and the applications of advanced methods in linear programming, including the revised simplex algorithm.
Competencies
1. Critical Thinking and Decision Making
- Enhance the ability to think critically about complex problems, making reasoned decisions based on the analysis of linear programming models and solutions.
2. Interpretation and Analysis
- Learn to interpret software outputs accurately and conduct sensitivity analyses, enabling the evaluation of alternative solutions and the assessment of their implications for decision making.
3. Multi-Criteria Decision Making
- Develop competencies in dealing with multi-criteria decision-making problems, understanding how to balance competing objectives and constraints in optimization scenarios.
Cross-Cutting Themes
1. Integration of Theory and Practice
- Bridging theoretical concepts with practical applications, ensuring students can apply linear programming techniques to solve problems in various domains such as logistics, finance, healthcare, and energy.
2. Use of Technology in Problem Solving
- Emphasize the importance of leveraging computational tools and software for solving and analyzing large-scale linear programming problems, preparing students for the technological demands of the industry.
Course Content
Introduction to Linear Programming (LP). The simplex method. Transportation, assignment and transshipment problems. Sensitivity testing, duality theory and its applications. Advanced methods in LP and revised simplex algorithm.
Prerequisite: MATH 260
Course Learning Outcomes
Please see Course Objectives.
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | Applying the knowledge of statistics, mathematics and computer to statistical problems and developing analytical solutions. | ✔ | |||
| 2 | Defining, modeling and solving real life problems that involve uncertainty, and interpreting results. | ✔ | |||
| 3 | To decide on the data collection technique, and apply it through experiment, observation, questionnaire or simulation. | ✔ | |||
| 4 | Analysing small and big volumes of data and interpreting results. | ✔ | |||
| 5 | Utilizing up-to-date techniques, computer hardware and software required for statistical applications; developing software programs and numerical solutions for specific problems when necessary. | ✔ | |||
| 6 | Taking part in intradisciplinary and interdisciplinary teamwork, using time efficiently, taking leadership responsibilities and being entrepreneurial. | ✔ | |||
| 7 | Taking responsibility in individual work and offering authentic solutions. | ✔ | |||
| 8 | Following contemporary developments and publications in statistical science, conducting research, being open to novelty and thinking critically. | ✔ | |||
| 9 | Efficiently communicating in Turkish and English to define and analyze statistical problems and to interpret the results. | ✔ | |||
| 10 | Having a professional and ethical sense of responsibility. | ✔ | |||
| 11 | Developing computational solutions to statistical problems that cannot be solved analytically. | ✔ | |||
| 12 | Having theoretical background and developing new theories in statistics, building relations between theoretical and practical knowledge. | ✔ | |||
| 13 | Serving the society with the expertise in the field. | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution