Optimizing Transportation Costs with a Hybrid Northwest Corner and Genetic Algorithm Strategy

Transportation problems are used in many real-world applications to determine the best way to ship products from supply to demand. Delivering certain goods from the supply to the demand is the subject of the transportation problem. The initial basic feasible solution is an essential step in solving the transportation problem's least total cost. Still, the current initial basic feasible solution techniques don't always offer an effective feasible solution that may reduce down the number of iterations needed to obtain the optimal solution. As a result, creating an improved initial basic feasible solution approach remains difficult. This study uses a two-stage solution strategy to solve the classical transportation problem. In order to provide an estimate for the transportation cost, the Northwest Corner Method is first applied to produce a feasible initially solution. In order to reduce the overall cost of transportation, a Genetic Algorithm is then used to enhance the initial solution. The hybrid approach makes use of the Genetic Algorithm's search capabilities for solution enhancing and the Northwest Corner Method's simple use and effectiveness for initialization. According to experimental data, the suggested approach converges to near-optimal solutions in an acceptable computational time and successfully lowers transportation costs when compared to conventional initial solutions.