Study On Multi-objective Optimization Approach Of Berth Scheduling In Container Terminal

The berths, as rare resources of container terminal, whose scheduling levels directly affect 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. During the berth scheduling in container terminal, a fast load-upload strategy is often taken by the organizer to shorten the service time of ships to improve the customer satisfaction and attract more ships to the port. However, a higher productive efficiency is prone to accident, such as over-loading operation of the equipment, fatigue working of personnel, high frequency of container-shifting, and the traffic jam, which increase the production cost and decrease the whole benefit of the port. These influences should be apparently considered in scheduling optimization.To solve the negative effects caused by the reduction of operation time, a kind of optimization thought, based on equilibrium conception, was brought forward. Emergence and development course of equilibrium conception were discussed; implication of equilibrium conception was summed up; equilibrium optimization problem of berth scheduling was analyzed. Based on characteristic of container liner, and influence of uncertain factors in berth scheduling, a least-cost model including berthing location, berthing time and loading speed, which was more suitable for the berth scheduling in container terminal, was established. The arithmetic was designed by the combination of human-computer interaction and genetic algorithm and an example was given to verify the validity of the model and feasibility of solution method. The results show that equilibrium schedule can be obtained by adjusting values of berthing location, berthing time and loading speed in the objective function. And solutions are more satisfied by combining with the expert experience and the genetic algorithm. The method can solve the equilibrium optimization problem and the results are more proximity to the practical situation.