Optimization Methods for Business Decision Making - Online Course
A 3-Day Livestream Seminar Taught by
Sri TalluriThursday, March 5 –
Saturday, March 7, 2026
10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
This seminar will provide you with a comprehensive understanding of decision analysis tools and their practical application in contemporary business environments. The primary emphasis is on optimization methodologies, which serve as powerful techniques for solving complex managerial and operational problems.
You will gain hands-on experience with a wide range of approaches, including linear and integer optimization, multi-objective optimization, network optimization, and data envelopment analysis. Each method will be contextualized through case applications across diverse domains such as manufacturing systems, financial decision-making, marketing analytics, supply chain management, and transportation and distribution planning.
To ensure both conceptual understanding and practical proficiency, the course incorporates extensive use of spreadsheet-based tools. By the end of the course, you will be well prepared to model, analyze, and solve structured decision problems, bridging the gap between theory and practice.
Starting March 5, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
Here is an example of the kinds of problems that you will learn how to solve in this seminar: A global consumer goods company (e.g., Procter & Gamble or Unilever) produces a range of products—such as shampoo, detergent, and soap—across multiple factories located in different regions. Each product requires specific raw materials that are available in limited quantities. Factories operate under production capacity constraints (e.g., machine hours, labor hours), and some factories are more efficient at producing certain products due to differences in productivity.
The company must meet customer demand for each product across various markets while also accounting for production and transportation costs incurred when producing and shipping products from factories to distribution centers. The goal is to minimize the total costs.
A linear optimization model for this problem would define decision variables as the quantity of each product produced at each factory and the quantity shipped from each factory to each distribution center. The objective function would be to minimize the total cost, while the constraints would include factory production capacity limits, raw material availability, and the requirement to fulfill market demand for each product. The solution to this model would determine the optimal production levels at each plant as well as the shipment quantities from factories to various markets.
Of course, your business problem may be much simpler than this one. But the methods you’ll learn can be applied to both simple and complex problems.
Here is an example of the kinds of problems that you will learn how to solve in this seminar: A global consumer goods company (e.g., Procter & Gamble or Unilever) produces a range of products—such as shampoo, detergent, and soap—across multiple factories located in different regions. Each product requires specific raw materials that are available in limited quantities. Factories operate under production capacity constraints (e.g., machine hours, labor hours), and some factories are more efficient at producing certain products due to differences in productivity.
The company must meet customer demand for each product across various markets while also accounting for production and transportation costs incurred when producing and shipping products from factories to distribution centers. The goal is to minimize the total costs.
A linear optimization model for this problem would define decision variables as the quantity of each product produced at each factory and the quantity shipped from each factory to each distribution center. The objective function would be to minimize the total cost, while the constraints would include factory production capacity limits, raw material availability, and the requirement to fulfill market demand for each product. The solution to this model would determine the optimal production levels at each plant as well as the shipment quantities from factories to various markets.
Of course, your business problem may be much simpler than this one. But the methods you’ll learn can be applied to both simple and complex problems.
Computing
Examples and hands-on exercises will be conducted in Microsoft Excel (SOLVER and Analysis TookPak add-ins). Proficiency with basic Excel is necessary. We will cover using Excel for modeling and data analysis during the seminar.
Examples and hands-on exercises will be conducted in Microsoft Excel (SOLVER and Analysis TookPak add-ins). Proficiency with basic Excel is necessary. We will cover using Excel for modeling and data analysis during the seminar.
Who should register?
This seminar is ideal for:
-
- Graduate students seeking to strengthen their analytical and problem-solving skills for decision-making in complex environments.
- Business professionals who want to enhance their ability to apply quantitative tools to real-world challenges.
- Aspiring managers, consultants, and analysts aiming to improve their proficiency in data-driven decision making and optimization.
- Students and faculty with an interest in analytics or decision sciences who wish to build a strong foundation in optimization techniques and their applications across industries.
To ensure you can fully engage with the material, the following background is recommended:
-
- Mathematical foundation: A working knowledge of college-level algebra and basic calculus.
- Statistics/probability: Familiarity with introductory statistics and probability concepts, such as means, standard deviation, and variance.
- Analytical mindset: Comfort with logical reasoning, quantitative problem-solving, and interpreting numerical results.
No prior coursework in optimization or advanced decision sciences is required. The course will build these skills step by step.
This seminar is ideal for:
-
- Graduate students seeking to strengthen their analytical and problem-solving skills for decision-making in complex environments.
- Business professionals who want to enhance their ability to apply quantitative tools to real-world challenges.
- Aspiring managers, consultants, and analysts aiming to improve their proficiency in data-driven decision making and optimization.
- Students and faculty with an interest in analytics or decision sciences who wish to build a strong foundation in optimization techniques and their applications across industries.
To ensure you can fully engage with the material, the following background is recommended:
-
- Mathematical foundation: A working knowledge of college-level algebra and basic calculus.
- Statistics/probability: Familiarity with introductory statistics and probability concepts, such as means, standard deviation, and variance.
- Analytical mindset: Comfort with logical reasoning, quantitative problem-solving, and interpreting numerical results.
No prior coursework in optimization or advanced decision sciences is required. The course will build these skills step by step.
Seminar outline
Day 1
- Introduction to optimization and decision analysis
- A foundational overview of optimization concepts and their role in effective decision-making.
- Linear programming
- Maximizing profit or minimizing cost within a set of continuous decision variables and constraints. Typical applications include product-mix planning, media selection in marketing, blending problems, ingredient-mix problems, and investment planning.
- Integer linear programming
- Linear optimization with discrete decision variables. Application areas include capital budgeting, production/employee scheduling, and project selection.
Day 2
- Multi-objective optimization
- Decision-making involving multiple goals, balancing trade-offs between competing objectives. Decision variables may be continuous or discrete. Applications include financial portfolio optimization, production planning, and marketing.
- Network optimization
- Optimizing flows within networks by minimizing costs or maximizing profits while adhering to various demand and supply constraints. Application areas include transportation, transshipment, and supply chain network design.
Day 3
- Data envelopment analysis (DEA)
- A performance measurement and benchmarking tool that evaluates the relative efficiency of decision-making units in the presence of multiple inputs and outputs. Application areas include healthcare, banking, and manufacturing.
Day 1
- Introduction to optimization and decision analysis
- A foundational overview of optimization concepts and their role in effective decision-making.
- Linear programming
- Maximizing profit or minimizing cost within a set of continuous decision variables and constraints. Typical applications include product-mix planning, media selection in marketing, blending problems, ingredient-mix problems, and investment planning.
- Integer linear programming
- Linear optimization with discrete decision variables. Application areas include capital budgeting, production/employee scheduling, and project selection.
Day 2
- Multi-objective optimization
- Decision-making involving multiple goals, balancing trade-offs between competing objectives. Decision variables may be continuous or discrete. Applications include financial portfolio optimization, production planning, and marketing.
- Network optimization
- Optimizing flows within networks by minimizing costs or maximizing profits while adhering to various demand and supply constraints. Application areas include transportation, transshipment, and supply chain network design.
Day 3
- Data envelopment analysis (DEA)
- A performance measurement and benchmarking tool that evaluates the relative efficiency of decision-making units in the presence of multiple inputs and outputs. Application areas include healthcare, banking, and manufacturing.
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.