**Transportation Problem:**

The transportation problem is a type of linear programming problem that deals with the optimal allocation of goods from several suppliers to several consumers at the minimum transportation cost.

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The objective is to determine the most cost-effective way to transport goods from sources to destinations, considering the capacities of the suppliers and the demands of the consumers.

**Methods for Finding Initial Basic Feasible Solution (IBFS):**

**Northwest Corner Method:**It starts in the northwest corner of the transportation tableau and distributes as much as possible, following a pattern until all supplies and demands are satisfied.**Least Cost Method:**This method selects the cell with the least transportation cost at each step, allocating as much as possible until all supplies and demands are met.**Vogel’s Approximation Method (VAM):**VAM considers the difference between the two lowest costs in each row and column and selects the cell with the highest difference, allocating as much as possible.

**Steps involved in Vogel’s Approximation Method (VAM):**

**Calculate Penalties:**Find the difference between the two lowest costs in each row and column. This represents the penalty for each row and column.**Identify the Cell with the Maximum Penalty:**Locate the cell with the highest penalty. If there’s a tie, choose arbitrarily.**Allocate as much as Possible:**Assign as many units as possible to the cell with the maximum penalty while considering the supply and demand constraints.**Adjust Supply and Demand:**Adjust the supply and demand values based on the allocation made in step 3. If a row is satisfied, reduce the supply; if a column is satisfied, reduce the demand.**Recalculate Penalties:**Repeat steps 1-4 until all supplies and demands are satisfied. After each allocation, recompute penalties for the remaining rows and columns.**Optimality Test:**Check for optimality. If all penalties are zero, the solution is optimal. Otherwise, return to step 2.

Vogel’s Approximation Method is effective in finding an initial feasible solution quickly and often leads to an optimal solution in fewer iterations compared to other methods. It takes into account the penalty information, making it more adaptive to the structure of the transportation problem.