The bulk port has a limited storage yard, during the entering port operation of cargos, there is the problem that how to give consideration to both the operating efficiency and arranging the reasonable storage of cargos in the storage yard with dynamic changes of cargos entering and leaving the port. In order to solve the problem, a Bulk Storage Assignment Algorithm in Bulk port based on Game theory (BSAABG) was proposed. Firstly, the storage assignment behavior was modelled as a dynamic game, and the satisfaction equilibrium was applied to analyze this game. Assuming that each batch of cargos has an expectation for assignment benefit, the game will reach satisfaction equilibrium when all cargos meet their expectations. Then, BSAABG was used to solve the model constructed above, and the convergence of the proposed algorithm was proved theoretically. Experimental results show that, when the number of cargo batches is 20, BSAABG can increase the average cargo satisfaction by 62.5% and 18.2% compared to the manual assignment method (simulated by Greedy Algorithm (GA)) and Storage Assignment algorithm Based on Rule (SABR) respectively, and has the storage assignment benefit 6.83 times and 3.22 times of those of GA and SABR respectively. It can be seen that the proposed algorithm can effectively improve the average cargo satisfaction and the storage assignment benefit.

Focusing on the traffic congestion problem in inland ports with vehicle transportation and large throughput, a collaborative routing method for operation vehicles in inland port based on game theory was proposed. Firstly, the interaction between the operation vehicles that simultaneously request route planning was modeled as a game with incomplete information and the idea of Satisfaction Equilibrium (SE) was applied to analyze the proposed game. It was assumed that every vehicle has an expected utility for routing result, when all vehicles were satisfied, the game achieved an equilibrium. Then, a collaborative routing algorithm was proposed. In this algorithm, firstly every vehicle selected the route according to greedy strategy, then all vehicles were divided into groups by the rule and vehicles in the group performed adaptive learning based on historical routing results to complete the game. The experimental results show that the collaborative routing algorithm reduces the average driving time of vehicles up to 50.8% and 16.3% respectively and improves the system profit up to 51.7% and 24.5% respectively compared with Dijkstra algorithm and Self-Adaptive Learning Algorithm (SALA) when the number of simultaneously working vehicles in port is 286. The proposed algorithm can effectively reduce the average driving time of vehicles, improve system profit, and is more suitable for the routing problem of vehicles in inland port.