چكيده به لاتين
The globalization of trade has fuelled the strong demand for maritime transport services, particularly containerized freight transportation. Clarkson’s Research Services has stated that world container trade during the last twenty years has on average increased by 7.8%, and container traffic grew in 2014 by 7 million TEU (Twenty-foot Equivalent Units), reaching 171 million TEU.
Spurred by such container trade growth, competition among container terminals has become acute, especially for geographically close container transshipment ports, such as the ports of United Arab Emirates and the Rajaee port of Iran. For this reason, the terminal operators must utilize the resources (i.e., berths and quay cranes) efficiently by taking both berth allocation and quay-crane allocation decisions simultaneously. As a matter of fact, these are among the most important decisions in a container terminal, given that efficient allocation of berths and quay cranes for incoming ships will enhance ship owners’ satisfaction (by reducing their waiting and handling times) and increase terminal productivity (by utilizing the resources more efficiently), leading to higher revenues.
Therefor, in marine transport, there is a growing need for optimization surveys, which are aimed at increasing the efficiency of the whole process. In this study, we examined different characteristics of container number 1 and 2 of Iranian Rajaee port. Afterwards, we developed three mathematical models and validate them with real data of our case study. First mathematical model studies terminal containter number 1 of Iranian Rajaee port. For this container terminal we developed a mathematical model to solve berth allocation problem and quay crane assignment problem in discrete and dynamic case simultaneously. Then , the proposed mixed integer mathematical model is coded via GAMS software. To check validity of the proposed model, historical data for 24 weeks of Iranian Rajaee port was provided. In average, it took less than 6 seconds for the proposed model to improve actual departure times by 6.9%.
For terminal number 2, the berth allocation problem, the quay crane assignment problem and the quay crane scheduling problem are simultaneously formulated into an integrated mathematical model. Afterwards, the proposed mixed integer mathematical model is coded via GAMS IDE/CPLEX software. The exact solver appears to need a huge amount of time to find the optimum solution, even for small and medium-sized problems. Hence, artificial intelligence, which is embedded in both imperialist competitive and genetic algorithms, is employed through a highly modified meta-heuristic method. This method is called a “hybrid imperialist competitive and genetic algorithm” (HICGA), and is designed to deal with the complexity of such problems. To check the validity of the proposed model and the performance of the designed HICGA method, historical data for 24 weeks from the Iranian Rajaee port were provided. The third model considered Greenhouse Gases pollution minimization as a secondary objective function. We solve this model with two meta-heuristic algorithms and compare their pareto solutions.
