Executive Development Programme in Modeling and Solving Optimization Problems

This course is designed for senior managers and executives aiming to enhance their strategic decision-making through advanced optimization techniques. Participants will gain the skills to model real-world problems using optimization models and tools, enabling them to make data-driven decisions that optimize resources and maximize efficiency.

They will learn to apply linear, integer, and stochastic programming, as well as network optimization, to solve complex business challenges. By the end, executives will be equipped to lead their teams in leveraging optimization for strategic advantage, driving innovation and improving organizational performance.

1. 1. Introduction to Optimization: Learners will study the basics of optimization, including types of problems, key terminology, and the role of optimization in real-world scenarios. They will gain foundational skills in formulating and analyzing simple optimization problems.
2. 2. Linear Programming: This module covers the principles of linear programming, including formulation, graphical methods, simplex algorithm, and sensitivity analysis. Learners will develop skills in solving linear programming problems and interpreting solutions.
3. 3. Integer Programming: Focusing on integer programming, learners will learn about the formulation of problems with integer variables, branch and bound techniques, and cutting plane methods. They will gain the ability to solve discrete optimization problems and understand the practical applications.
4. 4. Nonlinear Programming: This module introduces nonlinear programming, covering unconstrained and constrained optimization, gradient methods, and Newton’s method. Learners will develop skills in solving nonlinear optimization problems and using software tools for optimization.
5. 5. Network Flows: Learners will study network flow problems, including shortest path, maximum flow, and minimum cost flow problems. They will learn algorithms for solving these problems and understand their applications in logistics and supply chain management.
6. 6. Integer Programming Applications: This module explores practical applications of integer programming in areas such as scheduling, facility location, and resource allocation. Learners will apply integer programming techniques to solve real-world problems and develop solutions.
7. 7. Heuristics and Metaheuristics: Focusing on heuristic and metaheuristic approaches, learners will study methods like simulated annealing, genetic algorithms, and tabu search. They will gain skills in designing and implementing these techniques to solve complex optimization problems.
8. 8. Advanced Optimization Techniques: This module covers advanced optimization techniques such as mixed-integer nonlinear programming and stochastic programming. Learners will delve into the complexities of these methods and their applications in various domains.
9. 9. Optimization Software and Tools: Learners will be introduced to popular optimization software and tools, including Gurobi, CPLEX, and open-source alternatives. They will gain hands-on experience in using these tools to solve optimization problems and interpret results.
10. 10. Case Studies and Project Work: In this final module, learners will work on case studies and projects that involve modeling and solving real-world optimization problems. They will apply the knowledge and skills gained throughout the programme to develop comprehensive solutions.