نجمیه سادات صفرآبادی

  In container ports, the wharf plays a key role in determining the capacity of terminals and its construction is costly. Therefore, improving the efficiency of terminals is essential. One of the key challenges in port logistics is the allocation of wharf space to incoming ships based on their characteristics and container numbers. This study focuses on optimizing the allocation of wharf space and its scheduling, taking into account existing constraints, using an evolutionary algorithm i  ired by Levy Flight. The main objectives include reducing the cost and time of ship stays, and optimal allocation of berths in the planning horizon. The experiments carried out indicate the effect of the fast convergence rate of the proposed algorithm, known as Enhanced Levy Flight, compared to other meta-heuristic algorithms, as well as the reduction of costs due to delayed scheduling for incoming ships in the port terminal. In the stability analysis experiment, the proposed algorithm was able to achieve an average cost of ۴.۹۳۶ units in a specified time window, with ۵۵ iterations. This result was obtained by comparing the proposed algorithm to other baseline algorithms, including particle swarm optimization, simulated annealing, and changing objective functions. In the second to fourth experiments, changes in the number of ships, wharfs, and the impact of uncertain factors, such as weather and tidal levels, were investigated. In these experiments, the execution time of the algorithm was less than one second to a maximum of six seconds, and the wharf utilization efficiency reached a maximum of ۸۶%. Finally, in the fifth and sixth experiments, a functional comparison was made between the proposed algorithm and three other algorithms, as well as a priority processing approach based on the order of arrival, using two different datasets. In these experiments, the convergence rate of the Enhanced Levy Flight algorithm decreased rapidly and reached an average of ۰.۱۸۳۵ units, indicating the high speed of this algorithm in finding accurate solutions to the problem