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To get global solution in multi-depots vehicle transshipment scheduling problem (MDVTSP), MDVTSP models are established. Genetic algorithm and particle swarm hybrid optimization is established to solve MDVTSP. The optimization course is as follow: first set up chromosome vector to get goods’ transshipment point, and assign goods to vehicles. Second, establish tabu matrix for ant colony optimization (ACO) to get vehicle route. Then evaluate and filtrate vehicle scheduling results by optimization aim, circulate until meet terminate qualification. Illustration results show that Hybrid arithmetic is effective for multi-depots vehicle transshipment scheduling problem.

Through analyzing the characteristics of student tickets reservation, and under the condition of controlling the student tickets reservation information, a mathematic model of student ticket distribution model (STDM) is established. Particle swarm optimization (PSO) is established to solve STDM. In PSO, particle position matrix is set up, matrix's every column corresponds to a student, and matrix has two rows, the first row corresponds to train, and its elements are random train number that student will like to choose, second row correspond to date, and its elements are random date that student will like to choose. When students have the same train number and date pair, we can get train's students loads in one date. Through calculation example, this paper expounds the actual effect of the model.

Improved ant colony optimization (ACO) was applied to solve bin-packing problem. A tabu matrix bases on goods is established for ant colony optimization (ACO). Tabu matrix's every column corresponds to goods. Matrix has two rows, first row corresponds to goods load visit state, and second row corresponds to vehicle that ferries the goods. Ant state transfer probability is improved to solve bin-packing problem. According to ants tabu table, all the goods are searched by ants and satisfy the vehicle constrain. Illustrated with a practical case, ACO is feasible for bin-packing problem.

To get global solution in multi-depots vehicle scheduling problem (MDVSP), MDVSP models are established. Two-phase particle swarm optimization (TPPSO) is established to solve MDVSP. The optimization course are as follow: first phase, set up goods number dimension particle position vector, vector's every column corresponds to goods, vector elements are random vehicle serial number, thus we can assign goods to vehicles. Second phase, particle position matrix is set up, matrix's column number equal to vehicle freight goods number, every column corresponds to a goods, and matrix has two row, the first row correspond to goods start depot, second row correspond to end depot, matrix elements are random number between 0 and 1, matrix elements are sort ascending according to sort rules, we can get single vehicle route. Then evaluate and filtrate particles by optimization aim, circulate until meet terminate qualification. TPPSO can assign all freights to all vehicles and easy to get optimized solution.

A multi-depots single vehicle routing problem (VRP) model based on goods were established to get global solution for VRP. Particle position matrix based on goods was established for particle swarm optimization (PSO) to sovle VRP model, and every matrix column corresponded to one goods. Matrix had two rows, first row corresponded to goods start point, and second row corresponded to goods end point. Matrix elements were random number between 0 and 1, and matrix elements sort rules were established to get single vehicle route. Then evaluated and filtered particles by objective function until met terminate condition. In this VRP model strategy, vehicle route to all goods was searched to avoid local minima. Illustration results show that VRP model and algorithm are effective and offer a way to multi-depots single vehicle routing problem.