Resumen
In this work, we study the integrated berth and crane scheduling problem in a tidal port with multiple terminals, considering the uncertainties, tides, maximum coverage of cranes and interference between cranes. For coping with the uncertainties, a certain number of randomly generated samples are used to evaluate the solutions, and slack variables are introduced to reduce the impact caused by the variation in vessel arrival and crane operational efficiency. A novel nonlinear mixed integer programming model is first formulated for the problem to minimize the sum of expectation and variance of costs under all samples. An improved adaptive genetic algorithm, combining a simulated annealing mechanism and greedy construction strategy, is developed and implemented by MATLAB. The feasibility and validity of the algorithm and the benefits of multi-terminal collaborative scheduling strategy under uncertainty are evaluated through numerical experiments. The results show that the algorithm can obtain feasible scheduling solutions with higher quality. Compared to the strategy that considers either the uncertainty or the multi-terminal collaborative mechanism, the resulting solution considering both can effectively reduce the cost and improve the competitiveness of the port.