Today, the maritime transportation industry is significantly crucial in the countries' economic cycle, considering that about 90% of the freight transported is done through the sea. Meanwhile, container terminals are becoming increasingly important as a link between the sea and lands. Therefore, the quality of services offered at ports is crucial for accelerating the transportation process, responding promptly to its customer, and attracting new customers. The presented paper examines the optimum time to service ships at the container port berths, which is done by finding a logical-mathematical relation between contributing factors. The contributing factors including required service time, delay time, ship length, length of the berth, number of 20 or 40-inch containers discharged or loaded, number of equipment assigned to each berth, and berth water depth. Finally, the optimal service time is identified by determining the fitness function, initial population, and ultimately imposing constraints in developing the genetic algorithm (GA). Although the GA is used wildly due to powerful algorithms in optimization, in port optimization problem was not considered, which is the novelty of the paper. The optimization results show that 4323 containers were discharged, and 1020 containers were loaded for 5186 min using a single gantry crane. It means that an average of 0.97 min is needed for loading or unloading each container. Using two gantry cranes, this process can be done in 5100 min, with an average of 0.95 min for each container, while the needed time for three gantry cranes was 4908 min, with the average of 0.91 min for each container. Based on the paper results, two important keys to reducing waiting time with berth allocation are determining suitable access channel depths and increasing the number of berths which in this paper are studied and analyzed as practical solutions.
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Mahpour, A., Nazifi, A. & Mohammadian Amiri, A. Development of Optimization Model to Reduce Unloading and Loading Time at Berth in Container Ports. Iran J Sci Technol Trans Civ Eng (2021). https://doi.org/10.1007/s40996-021-00590-2
- Service time
- Genetic algorithm