Study On Container Berth-Quay Crane Allocation Optimazation Based On Multi-Objective Genetie Algorithm

As rare resources of container terminals, the berths'scheduling levels directly affect the efficiency of cargo-handling in terminal, sailing schedule of container liner and reputation of port enterprise, and reflect management level and comprehensive competitive power of terminal, In practice, berths scheduling together with the allocation of quay cranes, that is, according to a certain optimizing tactics and some constraints such as the berth, quay cranes are free or not, the terminal scheduling worker arranges berthing order, berth and the quality of loading-uploading quay cranes for the container vessels arrived. Comparing with the separated berth allocation or quay crane allocation, the berth-quay crane allocation makes the two as a whole system, which can efficiently reflects the relationship of each influence and each restriction, it corresponds with the actual terminal production organization better.The berth-quay crane allocation problem of container terminal is typical NP-hard problem, which is hardly solved by exact algorithm. At moment, most researches make the reduction of vessels'service time or the terminal's production cost as the goals, then sovle the problem by heuristic algorithm or global search evolutionary algorithm. But this kind of researches prefer to the single optimization or changing MOP into single optimization, the berth allocation optimization should solve the goals together, which has confluences on results of the algorithm. So it is apparent to consider the factors in the berth scheduling optimization.Considering the complexity of berth-quay crane allocation MOP's solution, the multi-objective genetic algorithm is adopted in this essay, and improved based on the study of predecessors, in order to solve the berth-quay crane allocation problem of container terminals. In model, starting from vessels'service demand and container terminal's comprehensive benefits, this paper chooses a multi-objective optimization model, simultaneously considering the stay time of vessels and the production cost as optimization objectives. To solve the model, improved multi-objective genetic algorithm is adopted. Firstly, the individuals, which are the feasible solutions of model, are expressed by genome. In order to ensure the dispersion and uniformity of Pareto optimum set, the initial population is produced by the way of random method. Secondly, a cross rule is given to deal with the restriction conditions as well as model's dependent variables are determined by a quay crane heuristic, to calculate the individuals values, and the individuals'fitness is evaluated by the Pareto ranking method. Further more, a strategy is suggested to choose a valid berth-quay crane allocation schedule from the Pareto optimum solutions. Finally, experiments are given to verify the efficiency of the model and method, while the results show that a satisfied solution can be obtained by the approach proposed in this thesis.