Operations Research Videos on YouTube
| Topic | Links |
| YouTube search result for "operations research" |
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| The Simplex Method - Finding a Maximum / Word Problem Example (5 Parts) PatrickJMT |
Part 1 Part 2 Part 3 Part 4 Part 5 |
| Fundamentals
of Operations Research Professor G. Srinivasan Department of Management Studies India Institute of Technology, Madras 22 Lectures Lec 1: Introduction to Linear Programming Formulation Lec 2: Linear Programming Formulations (Contd...) Lec 3: Linear Programming Solutions- Graphical Methods Lec 4: Linear Programming Solutions - Simplex Algorithm Lec 5: Simplex Algorithm-Minimization Problems Lec 6: Simplex Algorithm - Initialization and Iteration Lec 7: Simplex Algorithm - Termination Lec 8: Introduction to Duality Lec 9: Primal Dual Relationships, Duality Theorems Lec 10: Dual Variables and the Simplex Tables Lec 11: Simplex Algorithm in Matrix Form - Sensitivity Analysis Lec 12: Sensitivity Analysis Transportation Problem (Intro...) Lec 13: Transportation Problems Lec 14: Transportation Problem-Optimal Solutions Lec 15: Transportation Problem-Other Issues Lec 16: Assignment Problem - Hungarian Algorithm Lec 17: Other Issues - Introduction to Dynamic Programming Lec 19: Dynamic Programming - Continuous Variables Lec 20: Dynamic Programming - Linear and Integer Problems Lec 21: Inventory Models - Deterministic Models Lec 22: Inventory Models - Discount Models |
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Advanced Operations Research Professor G. Srinivasan Department of Management Studies India Institute of Technology, Madras 29 Lectures Lec 1: Introduction and Linear Programming Lec 2: Revised Simplex Algorithm Lec 3: Simplex Method for Bounded Variables Lec 4: One Dimensional Cutting Stock Problem Lec 5: One Dimensional Cutting Stock Problem(Contd) Lec 6: Dantzig-Wolfe Decomposition Algorithm Lec 7: Dantzig-Wolfe Decomposition Algorithm Primal-Dual Algorithm Lec 8: Primal-Dual Algorithm Lec 9: Goal Programming-Formulations Lec 10: Goal Programming Solutions Complexity of Simplex Algorithm Lec 11: Complexity of Simplex Algorithm(Contd) Integer Programming Lec 12: Integer Programming-Formulations Lec 13: Solving Zero-One Problems Lec 14: Solving Zero-One Problems(Contd) Lec 15: Branch And Bond Algorithm For Integer Programming Lec 16: Cutting Plane Algorithm Lec 17: All Integer Primal Algorithm Lec 19: Network Models Lec 20: Shortest Path Problem Lec 21: Successive Shortest Path Problem Lec 22: Maximum Flow Problem Lec 23: Minimum Cost Flow Problem Lec 24: Traveling Salesman Problem(TSP) Lec 25: Branch and Bound Algorithms for TSP Lec 26: Heuristics for TSP Lec 27: Heuristics for TSP(Contd) Lec 28: Chinese Postman Problem Lec 29: Vehicle Routeing Problem |
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Linear programming and Extensions Professor Prabha Sharma Department of Mathematics and Statistics India Institute of Technology, Kanpur 40 Lectures Lec 1: Introduction to Linear Programming Problems Lec 2: Vector space, Linear independence and dependence, basis Lec 3: SMoving from one basic feasible solution to another, optimality criteria Lec 4: Basic feasible solutions, existence & derivation Lec 5: Convex sets, dimension of a polyhedron, Faces, Example of a polytope Lec 6: Direction of a polyhedron, correspondence between bfs and extreme points Lec 7: Representation theorem, LPP solution is a bfs, Assignment 1 Lec 8: Development of the Simplex Algorithm, Unboundedness, Simplex Tableau Lec 9: Simplex Tableau & algorithm ,Cycling, Bland's anti-cycling rules, Phase I & Phase II Lec 10: Big-M method,Graphical solutions, adjacent extreme pts and adjacent bfs Lec 11: Assignment 2, progress of Simplex algorithm on a polytope, bounded variable LPP Lec 12: Bounded variable, Revised Simplex algorithm, Duality theory, weak duality theorem Lec 13: Weak duality theorem, economic interpretation of dual variables Lec 14: Examples of writing the dual, complementary slackness theorem. Lec 15: Complementary slackness conditions, Dual Simplex algorithm, Assignment 3 Lec 16: Primal-dual algorithm Lec 17: Problem in lecture 16, starting dual feasible solution, Shortest Path Problem Lec 18: Shortest Path Problem, Primal-dual method, example Lec 19: Shortest Path Problem-complexity, interpretation of dual variables Lec 20: Assignment 4, postoptimality analysis, changes in b, adding a new constraint Lec 21: Parametric LPP-Right hand side vector Lec 22: Parametric cost vector LPP Lec 23: Parametric cost vector LPP, Introduction to Min-cost flow problem Lec 24: Mini-cost flow problem-Transportation problem Lec 25: Transportation problem degeneracy, cycling Lec 26: Sensitivity analysis Lec 27: Sensitivity analysis Lec 28: Bounded variable transportation problem, min-cost flow problem. Lec 29: Min-cost flow problem Lec 30: Starting feasible solution, Lexicographic method for preventing cycling Lec 31: Assignment 6, Shortest path problem, Shortest Path between any two nodes Lec 32: Min-cost-flow Sensitivity analysis Shortest path problem sensitivity analysis Lec 33: Min-cost flow changes in arc capacities , Max-flow problem, assignment 7 Lec 34: Problem 3 (assignment 7), Min-cut Max-flow theorem, Labelling algorithm Lec 35: Max-flow - Critical capacity of an arc, starting solution for min-cost flow problem Lec 36: Improved Max-flow algorithm Lec 37: Critical Path Method (CPM) Lec 38: Programme Evaluation and Review Technique (PERT) Lec 39: Simplex Algorithm is not polynomial time- An example Lec 40: Interior Point Methods |
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Convex Optimization I Professor Stephen Boyd Electrical Engineering Department Stanford University Lectures 1 through 19 Videos 1 through 19 Convex Optimization II Professor Stephen Boyd Electrical Engineering Department Stanford University Lectures 1 through 18 Videos 20 through 37 |
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