For the same Oil Company, where we developed an inventory logistics optimization model, we implemented an analytical solution to simulate the different cargo movements, simulating various modes of transportation such as trains, barges and trucks.
In order to meet the needs of the Oil Company, where different transportation alternatives between a point of origin (A) and a point of destination (B) in the supply network are to be observed, it is necessary to find a number k of shortest single paths between these points. The length of the paths is given by variables such as time or the monetary cost of the routes, so that the requirements would be met.
However, when k is equal to 1, i.e., the aim is to find the shortest path between points A and B, there are efficient algorithms for the search, such as the one proposed by Dijkstra (1959). On the other hand, when we want to search for more paths, there are different approaches. For example, Yen (1971) proposes an algorithm in which he finds the second shortest path from deviations from the shortest path, then to find the third shortest path he considers deviations from the second shortest path, and so on until the desired number of paths is found.
The organization's inventory logistics leaders have a tool that allows them to simulate the network of inventory collection points. With this, it is possible to simulate the transportation of cargo by selecting the points of origin and destination to visualize the cost and time of each means of transportation (rail, river and land).
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