An effective heuristic algorithm for multiple vehicle routing problem

In the globalization of the economy, the shipping costs of a product is a significant component of an organization's overall expenses. A well-planned delivery schedule can reduce transportation costs of industries; however, planning an efficient routing can be complex and time-consuming. This research presents a Heuristic approach for large-scale, multiple-vehicle routing problems, using a grouping technique with a modified minimal spanning tree with its implementation in computer programming. For testing the practicability of the proposed algorithm, various cases are created and compared with the outputs of the mostly used K-mean Clustering algorithm. The results of test cases show that the proposed heuristic provides consistent good-quality of solutions within a smaller elapsed time. The distance of the Heuristic method was on an average 22% less and the elapsed time for the Heuristic method was on an average 58% less than the outputs of K-means Clustering model using the real-world applied cosine distance for large size problems. Additionally, some more modifications in the regrouping section of programming might lead to better outcomes. The successful development of the heuristic approach can be applied to many different kinds of combinatorial optimization, such as waste collection management, and package delivery