
Here will learn the algorithm of Transportation Problem. In the phase 1, we obtain the Initial Feasible Solution or IFS for the problem.
Master the transportation problem algorithm by applying Vogels approximation method to obtain an initial feasible solution, test for optimality, and explore multiple optimal patterns with practical examples.
This lecture covers the transportation problem, phase one and phase two, and obtaining an initial feasible solution with Vogels approximation method, followed by optimality checks and degeneracy handling.
Learn to solve transportation problems using phase i and phase ii, applying northwest corner, least-cost, and vogels approximation methods, with degeneracy handling, optimality tests, and loop adjustments.
I am a professor of Operations Research, teaching this subject from 1986 onwards. Linear Programming, Assignment Problem, Transportation Problem, Networking Techniques, Game Theory are some of the major algorithms in Operations Research. My attempt, over the years has been to teach without involving great deal of mathematics, so that the student without any math background can also understand the algorithm easily. In the initial phase I launched the courses on Linear Programming, Assignment Problem, Transportation Problem, Sequencing at one go. This 10 hour video course will give you complete insight into solving a Transportation Problem. Both minimization and maximization cases will be covered, including the more complex degenerate problems. The initial Feasible solution to a transportation problem will be obtained by one of the three methods namely North West Cornet Rule, Least Cost Method or Vogel's Approximation Method. In the second phase, we will cover the MoDi or Modified Distribution Method. The solution to the unbalanced Transportation Problem has been covered. A representative problem of applying the algorithm to a scheduling problem has been covered in the course. In the case of the this algorithm, step by step approach to solving the problems has been introduced, starting with what I call as ‘prechecks’. The idea is that the student should be able to formulate and solve the problem on her own. While both these algorithms are for minimization, the problems for maximization can also be solved by converting them into minimization first. Examples of this effect are covered. I hope that the student will be able to formulate a given situation as one of these algorithm problems and solve it on his own.