Two-Stage Model of Dry Port Location Using Hub Location Problem and Cost Function (in Persian)

In recent years the increase in the quantity of cargo in the main ports of Iran has finally led to the development and establishment of dry ports in the country. Among the important advantages of the dry ports are: reducing the sedimentation of goods, increasing the share of rail transport and reducing road traffic, reducing the costs of transporting container cargo, and speeding up customs operations. The establishment of a dry port requires facilities and equipment for the entry and clearance of goods, changing the type of transportation, warehouse spaces, and customs operations, the cost of which varies according to the capacity level. This article is written to locate dry ports for Shahid Rajaei and Imam ports. The location of the dry port was modeled based on the location of the hub and the cost function, and because it is mixed linear programming, it has been solved by the exact simplex algorithm method. The optimum location of the dry port for Rajaei and Imam ports was done once separately for each and once again in a combined form. According to the results, the smaller the volume and dispersion of cargo origins/ destinations, the less the need for a dry port and the more optimal the use of the capacity of the main ports. Accordingly, the optimal possible state for Rajaei port is to use 37% of its capacity and for Imam port, it is 74%. The multiplicity of dry ports with greater spatial distribution in the country is more beneficial for the users. If the problem is solved for all ports in the country, due to the 90% share of Rajaei port in attracting container cargo, the optimal places for the construction of the dry port will be Aprin and the special economic zone of Sirjan, and the load between the mentioned hubs and the main port will be proportional to the cost of transportation. That is, the optimal dry port of Bandar Rajaei can be generalized to the macro scale of the country due to its significant role in the share of container cargo.