| With the acceleration of the process of economic globalization, the global container transportation is developing rapidly and the major container docks worldwide are facing the challenge of drastic increase in handling capacity. The maximum carrying capacity and minimum of in-dock time of container ships require higher port loading and unloading efficiency. The oversized staff, vehicles and storage yards equiped for that purpose greatly increase operation cost and management difficulty. The emergence of container automatized dock is aimed at reducing the operating staff, operation cost and increasing working efficiency in a steady and reliable way in order to fully restructure the traditional dock loading and unloading process which is low efficient into one that is more efficient and intelligent and farseeing.The factors to the efficiency of container dock loading and unloading are many, such as the allocation of the resources of the equipment, the plan of yard, the assignment and allotment of berth, quayside container cranes, and container truck as well as the route of dock equipment movement. The optimization of the route of quayside container cranes and gantry cranes is a key joint in automizing cranes and then vital to the the realization of the high efficiency intelligent 3D container dock.The several factors to the route design and the non-linear model make it difficult to use the traditional method of optimization.However, in recent years, Genetic Algorithm has been put into successful applications in wide fields for its simplicity, easy operation, low demand, concurrence and integration. Thus, this thesis introduces Genetic Algorithm into research on port loading and unloading to better deal with the route planning problem of container operation.The thesis first establishes the single objective mathematical model of route planning for container operation with the shortest route as the objective function and with no collision as constraint. Then it improves the standard Genetic Algorithm in order to design the codes, genetic operators, and choosing methods suitable to the model, and it makes the computer programs to simulate solution-seeking process. Through analyzing the simulation results, it proves the rationality, effectiveness and practicableness of single objective problem of container operation route planning with improved Genetic Algorithm.But in practical process of container operation, safe distance is a more focused problem. In order to avoid accidents of container overturns, "door frame" operation is even conducted compulsorily. So based on the research in last chapter, the thesis also takes safety distance as the objective function, and expands the research contents to multi-objective optimal problem of route planning for container operation. Then it converts the shortest route to the equivalent maximum objective function and establishes the mathematical model with the maximum dual objectives of route and safe distance.Then it designs the Pareto resolution set of the optimal route from every offspring in genetic evolution. It finally gets the route Pareto resolution set satisfying the contraint by updating the resolution set incessantly, and finds the optimal solution by compromising.lt proves the rationality of the model through analyzing the simulation result and effectiveness and practicableness of multi- objective problem of container operation route planning with the Pareto Genetic Algorithms designed.The thesis does preliminary technological exploration on the theory of the loading and unloading of the contaioner crane.The mathematical mode! of route planning present here has simplified,but still neetto be improved by considering more real life parameters such as trolley velocity.the control of hand handler, electrical machinery.etc.Only one working environment is simulated here and the following research will be on the optimal route plan and its solution in working environments of the combination of different tasks,goods.and requirements. |
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