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.

Although ontology-based dynamic requirement traceability methods can improve the accuracy of trace links compared with Information Retrieval (IR), but it is rather complicated and tedious to construct a reasonable and effective ontology, especially domain ontology. In order to reduce time cost and labor cost brought by the domain ontology construction, a Modifier Ontology-based Keyword Semantic Judgment Method (MOKSJM) which combined modifiers with general ontology was proposed. Firstly, the collocation relationship between keywords and modifiers was analyzed. Then, the semantics of keywords were determined by combining modifier ontologies with rules, so as to avoid the bias of dynamic requirements traceability results caused by the polysemy of keywords. Finally, based on results of the above analysis, the semantics of keywords were adjusted and reflected by similarity scores. The number of modifiers is small in the requirements document, design documents, etc., so the time cost and labor cost brought by establishing the modifier ontology is relatively small. The experimental results show that compared to domain ontology-based dynamic requirement traceability method, MOKSJM has a small gap in precision with the same recall rate, and when compared to Vector Space Model (VSM) method, MOKSJM can effectively improve the accuracy of the requirements traceability result.

Information Retrieval (IR) technology is widely used in automatic tracing from software documents to source codes, but Chinese document and source code are written in different languages, which leads to low accuracy of automatic tracing by using IR. In view of the above problems, an automatic tracing method of Chinese document to source code based on version control was proposed. Firstly, the similarity score between the documents and the source code was calculated by information retrieval method combined with text-to-source heuristic rules. Then the score was modified by the version update information which was submitted to the version control software during software development and maintenance. Finally, the tracing relationship between the Chinese document and source code was determined according to the set threshold. The experimental results show that the precision and recall of the proposed method have a certain improvement compared with the traditional IR method, and the tracing relationship missed in the traditional IR method can be extracted.