Two-stage decision optimization of freight station location-allocation problem: A case study in Yan'an, China

Freight station location-allocation problem was analyzed, which was a kind of capacitated multi-facility location problem (MFLP) with stochastic demands (SMCFLP). A two-stage decision optimization approach was developed for solving this problem, in which the first stage dealt with the location decision that would be made before the construction of station, and in the second stage, the allocation decision was ideally made before actual operation but after station construction. Both stages are solved simultaneously by a single model. A corresponding mathematical model with capacity constraint was established based on scenario approach. The construction and operating costs in the model were assumed to be proportional to station capacity. Meanwhile, the constraints considered periodic demand of plan, closer to the actual situation. A genetic algorithm (GA) was developed using symbolic coding to search for the optimal solutions, where the coding method reduced the search scope and computational complexity and some restrictions of GA were eliminated. Aimed at one national level transportation hub planning problem in China, with two-stage optimization approach, based on actual statistical data, specific mathematical model is established and solved by the designed algorithm. Many kinds of optimization result of the location-allocation under different scenarios and related analyses are performed.