Deployment optimization of multi-cargo inland ports with environmental constraints

A dual-objective multi-cargo inland port deployment optimization model is built. One of the objectives is minimum overall costs for construction, operation, transport, transit and time, and the other is minimum pollutant emissions. Three algorithms are designed: conventional genetic algorithm; the combination of genetic algorithm and simulated annealing algorithm; and the non-dominated sorting-based genetic algorithm. For verification, the deployment planning is tried for tree provinces in northeast China and the eastern cities of Inner Mongolia with the model. The finding of the experiment is cited that when the time sensitive factor of the cargo is low, larger part of the cargo goes to seaports to be shipped to destination via inland ports, while if the time sensitive factor of the cargo goes higher, more cargo will go to seaports by highway.