Course pre-requisite(s): basics of MS Excel

Course Overview

This course is tailored for university students eager to master decision-making techniques and optimization strategies. It is designed to unlock the power of quantitative tools to tackle real-world challenges in business, economics, and finance.
The course's primary goal is to equip students with the skills to model complex decision-making problems and confidently solve them using proven methods. A key focus is hands-on experience with MS Excel and its Solver add-in to solve optimization problems efficiently.

To make the learning process interactive and engaging, the course incorporates simulation games that bring optimization challenges to life. Students will compete in tasks such as inventory management or route optimization, fostering a deeper understanding of decision-making concepts in a fun and inspiring environment.
The course culminates in a team project, where participants will apply their knowledge to solve real-life case studies. Teams will build mathematical models, employ advanced techniques, and present their solutions to address complex decision problems. This collaborative experience prepares students for professional problem-solving scenarios while enhancing teamwork and communication skills.

Learning Outcomes

After completing the course, students will be able to:
-build optimization models for decision problems that occur in management, economics, and finance.
-choose suitable methods to solve optimization problems representing decision-making situations.
-find the solution using software (Solver, MS Excel)
-interpret and validate the results

Course Content

1. Basics of Optimization and Decision Analysis
•Modeling real-world problems using linear programming (e.g., resource allocation, production planning).
•Solving optimization problems in MS Excel Solver or other tools like Python or R.
•Practical applications: cost minimization, profit maximization.
2. Data-Driven Decision Making
•Using data analysis to support decision-making (Business Intelligence).
•Applying statistical and predictive methods to simulate future scenarios.
•Hands-on practice with tools like MS Excel or Power BI.
3. Optimization in Logistics and Transportation
•Techniques: MODI algorithm for transportation problems, Traveling Salesman Problem (TSP).
•Applications: optimizing warehousing, route planning, product distribution.
4. Project Management
•Using the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT).
•Simulating resource-constrained project management.
•Case studies: planning large-scale projects and managing risks.
5. Combinatorial Optimization
•Solving assignment problems (e.g., using the Hungarian algorithm).
•Examples: scheduling, workforce planning.

6. Risk and Uncertainty in Decision Making
•Modeling risks with Monte Carlo simulation.
•Stochastic optimization models.
•Applications: investment decision-making, portfolio analysis.
7. Sustainability and Modern Trends
•Optimization in the context of sustainable development (e.g., minimizing emissions, circular economy).
•Introduction to AI in decision-making processes (e.g., basic machine learning for predictions).

Instructional Method

The classes include both theoretical training and problem-solving sessions. Students will discuss the solutions of case studies. Advanced problems will be solved in the form of team projects.

Required Course Materials

Students will be provided with all course materials including training problems for the seminars (assignments, data, spreadsheets), lecture slides, and other documents providing theoretical background. Technical equipment requirements: computers with MS Excel.

Assessment

•Team project: 40%
•Class activities 20%
•Final test 40%