Linear Programming (LP) is a fundamental branch of OR that deals with optimizing the allocation of limited resources to achieve specific goals. This course, "Operations Research - Linear Programming," provides an in-depth exploration of the theory, methods, and applications of linear programming in real-world scenarios.

This is an advanced course that focuses on the critical aspect of sensitivity analysis in optimization models and the concept of shadow prices in linear programming. Sensitivity analysis plays a crucial role in understanding how changes in problem parameters impact the optimal solution, providing valuable insights for decision-makers. This course offers students an in-depth exploration of sensitivity analysis techniques and shadow prices, equipping them with the skills to assess and interpret the robustness of optimization solutions in diverse real-world scenarios.

Integer Linear Programming (ILP) is a powerful extension of linear programming that addresses decision-making problems involving discrete variables. This course, "Operations Research - Integer Linear Programming," offers a comprehensive exploration of the theory, techniques, and applications of integer linear programming in various real-world scenarios. Students will learn how to model, solve, and interpret integer programming problems to make optimal decisions in complex decision-making environments.

This is a comprehensive course that focuses on the application of transportation models in optimizing the distribution and allocation of resources within supply chains and networks. This course introduces students to various transportation problems and equips them with the skills to formulate, analyze, and solve complex logistical challenges using transportation models. Through a combination of theoretical concepts, practical examples, and hands-on exercises, students will gain proficiency in designing efficient transportation systems and making informed decisions to enhance resource utilization and reduce costs.

This is an advanced course that focuses on the theory, methods, and applications of assignment models in various decision-making contexts. Assignment models are valuable tools for optimizing the allocation of resources to tasks or individuals in scenarios with one-to-one assignments. This course introduces students to the principles of assignment models, equipping them with the skills to formulate, solve, and interpret assignments for optimal resource allocation. Through a combination of theoretical concepts, practical exercises, and case studies, students will develop proficiency in using assignment models to solve real-world optimization problems.

This is an advanced course that delves into the theory, methods, and applications of network models in solving complex optimization problems involving interconnected systems. Network models provide a powerful framework for analyzing and optimizing flows, routes, and connections within various types of networks. This course introduces students to different types of network models, such as shortest path, minimum spanning tree, maximum flow, and more, equipping them with the skills to formulate, analyze, and solve optimization problems in interconnected systems.

This is a comprehensive course that focuses on the application of operations research techniques in managing and optimizing project activities and resources. Project management plays a crucial role in achieving organizational goals by efficiently planning, scheduling, and controlling projects. This course introduces students to project management concepts, tools, and methodologies grounded in operations research principles. Through a blend of theoretical knowledge, practical case studies, and hands-on exercises, students will acquire the skills to effectively plan, execute, and manage projects to successful completion.

This is an advanced course that delves into specialized topics within linear programming, offering students a deeper understanding of unique scenarios and challenges that arise in real-world optimization problems. This course explores various special cases of linear programming, providing students with the tools to formulate, analyze, and solve complex optimization problems with specific constraints. Through a combination of theoretical concepts and practical applications, students will learn to navigate intricate decision-making scenarios efficiently.