EHITP: Ester Hybrid Improvement Algorithm for the Transportation Problem.

Background: The Transportation Problem (TP) is a detailed model in operations study with applications in logistics, supply chain management, and resource allocation. The classical IBFS methods including North-West Corner, Least Cost and Vogel's Approximation have competitive computational efficiency, but they are very sensitive to the structure of the problem and usually lead to a solution that is far from the global optimum. Classic enhancement strategies like the Generalized Distribution (MODI) and Stepping-Stone (SS) approaches have low computational complexity but may fall into a local optimum quickly, which makes them ineffective in large-scale or unbalanced problems. Methods: We propose the first generic hybrid algorithm, called Ester Hybrid Improvement for Transportation Problem (EHITP), which was developed with the aim of mitigating the shortcomings of traditional IBFS-based methods. To overcome the local minima problem, the proposed EHITP framework combines adaptive perturbation procedures and guided neighborhood search methodologies to broaden the solution space. Results: Initial experiments on benchmark and synthetically created datasets show that EHITP obtains a much less total transportation cost relative to the classical IBFS and improved MODI/SS methods. These features lead to a more robust method, stable solutions over iterations, and convergence across a wider range of problem sizes and structures. Conclusions: The findings show EHITP serves as a more reliable, scalable, and expense-effective solution to transportation issues. The balance this algorithm achieves between the quality of the solution it produces, and its computational efficiency makes it a potential candidate for real life applications in topics such as distribution chain and economic resource allocation.