Nowadays, there is much attention for planning of container terminals in the global trade centers. The high cost of Quay Cranes motivates both scholars and industrial practitioners especially in the last decade to develop novel optimization models to address this dilemma. This study proposes a coordinated optimization model to cover both Quay Crane Scheduling Problem ((QCSP)) and Quay Crane Assignment Problem (QCAP) as among the first attempts in this area. Another main contribution of this paper is to apply a recent nature-inspired algorithm called Red Deer Algorithm (RDA). The RDA revealed its performance for a variety of combinatorial optimization Problems in different real-world applications. This is the first attempt in the literature to employ this recent metaheuristic to solve the proposed Coordinated Quay Crane Scheduling and Assignment Problem (CQCSAP). Finally, an extensive comparison discussion is considered to reveal the main benefits of the proposed optimization model and solution algorithm.

The last decade has seen the important role of container terminals in the functionality of global trade centers. From another point of view, the high cost of Quay Cranes (QCs) is a motivation for solving a set of real-world Problems including Quay Crane Assignment Problem (QCAP) and the Quay Crane Scheduling Problem ((QCSP)) in the hotspot of research. The main innovation of this proposal is to integrate both QCAP and (QCSP) to improve the performance of QC with emphasis on optimization, i. e., QCASP. A real case study in Iran was applied to validate the proposed Problem which was formulated by a Mixed Integer Linear Programming (MILP). Due to the inherent complexity of the Problem proposed in the real-world cases, the Teaching-Learning-Based-Optimization (TLBO) algorithm was used to  nd an optimal/global solution in a reasonable span of time. The applied TLBO was tuned by Taguchi method and validated in small instances in comparison with an exact method. The computational results showed that our proposed TLBO algorithm could solve Quay Crane Assignment and Scheduling Problem (QCASP), especially in large-size instances, successfully. Finally, some managerial implications are recommended to consider the benefits of the proposed methodology and the high-efficiency of the algorithm regarding the real case study presented.

In recent years, with the increasing growth of globalization and facilitation of freight transportation by sea, the importance of maritime transportation has become more than ever. Since ports are important hubs in the maritime network and have a direct impact on the maritime transportation process, the importance of port operations planning, minimizing the cost of operations, has become increased. One of the port planning subjects is seaside operations. seaside operations include three subjects: Berth Allocation Problem, Quay Crane Assignment Problem, and Quay Crane Scheduling Problem. This paper intends to develop and solve integrated model for these three subjects to achieve an optimal plan for seaside operations. This paper uses the heuristic algorithm, dynamic programming, to solve Quay Crane Scheduling Problem and 5 metaheuristic algorithms (GA, PSO, ACO, ICO, TS) to solve berth allocation and Quay Crane assignment Problem and finally compares the performance of these algorithms. The results show the better performance of the genetic algorithm in solving seaside operations Problems. To validate the results of this paper, the data of two container terminals of Shahid Rajaei port have been used.

One of the port planning Problems that has been noticed in many papers and research is the berth planning Problems. Berth planning includes two sub-Problems; Berth Allocation Problem (BAP) and Quay Crane Assignment Problem (QCAP). This paper develops one mathematical model by integrating these two sub-Problems. The berth allocation and Quay Crane assignment model (BAQCAP) is solved by two metaheuristic algorithms; Taboo Search (TS) and Ant Colony Optimization (ACO). On the other hand, the berth plan is located in a disturbed environment; unexpected events may occur during the execution of the plan, making it infeasible or challenging to do the initial berth plan. These unexpected events are known as disruptions, which can impose additional costs on the port or make the initial berth plan infeasible. For this reason, The primary purpose of this paper is on the berth plan recovery in the disrupted situation. the Berth plan is recovered with two methods; Global recovery and local recovery. This paper compares global and local recovery to identify the optimal method for berth plan recovery. The numerical results show the optimal performance in the local recovery method. In this paper, the data from Shahid Rajaei port is used.

In this research, an integrated approach is presented to simultaneously solve Quay Crane Scheduling and yard truck Scheduling Problems. A mathematical model was proposed considering the main real-world assumptions such as Quay Crane non-crossing, precedence constraints and variable berthing times for vessels with the aim of minimizing vessels completion time. Based on the numerical results, this proposed mathematical model has suitable efficiency for solving small instances. Two versions of imperialist competitive algorithm (ICA) are presented to heuristically solve the Problem. The grouping version of algorithm (G-ICA) is used to solve the large-sized instances based on considering the allocation of trucks as a grouping Problem. Effectiveness of the proposed metaheuristics on small-sized Problems is compared with the optimal results of the mathematical model. In order to compare the efficiency of the proposed algorithms for large-sized instances, several instances were generated and solved, and the performance of algorithms has been compared with each other. Moreover, a simulated annealing (SA) algorithm is developed to solve the Problem and evaluate the performance of the proposed ICA algorithms. Based on the experimental results, the G-ICA has a better performance compared to the ICA and SA. Also an instance of a container terminal in Iran has been investigated which shows that the proposed model and solution methods are applicable in real-world Problems.

A bi-objective mathematical model is developed to simultaneously consider the Quay Crane and yard truck Scheduling Problems at container terminals. Main real-world assumptions, such as Quay Cranes with non-crossing constraints, Quay Cranes’ safety margins and precedence constraints are considered in this model. This integrated approach leads to better efficiency and productivity at container terminals. Based on numerical experiments, the proposed mathematical model is effective for solving small-sized instances. Two versions of the simulated annealing algorithm are developed to heuristically solve the large-sized instances. Considering the allocation of trucks as a grouping Problem, a grouping version of the simulated annealing algorithm is proposed. Effectiveness of the presented algorithms is compared to the optimal results of the mathematical model on small-sized Problems. Moreover, the performances of the proposed algorithms on large-sized instances are compared with each other and the numerical results revealed that the grouping version of simulated annealing algorithm outperformed simulated annealing algorithm. Based on numerical investigations, there is a trade-off between the tasks’ completion time and the cost of utilizing more trucks. Moreover increasing the number of YTs leads to better outcomes than increasing the number of QCs. Besides two-cycle strategy and using dynamic assignment of yard truck to Quay Cranes leads to faster loading and unloading procedure.

This paper presents a novel, mixed-integer programming (MIP) model for the Quay Crane (QC) Scheduling and assignment Problem, namely QCSAP, in a container port (terminal). Obtaining an optimal solution for this type of complex, large-sized Problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper, thus, proposes a genetic algorithm (GA) to solve the above-mentioned QCSAP for the real-world situations. Further, the efficiency of the proposed GA is compared against the LINGO software package in terms of computational times for small-sized Problems. Our computational results suggest that the proposed GA is able to solve the QCSAP, especially for large sizes.

According to the figures issued by UNCTAD, containerized trade is forecasted to grow by an average annual rate of 5.32 percent between the years 2003 and 2025. This paper studies yard Crane Scheduling Problem between different blocks for a container terminal. The purpose is to minimize the total travel time of the Cranes among the blocks and also, the total delayed workload in each block at different time periods. In other words, the forecasted workload within each planning period, additional Crane capacity or insufficient Crane capacity for each block, the time when Cranes should be deployed and the routes of Crane movements, all should be determined in a way that satisfies our objectives. Therefore, the Problem is formulated as a mixed integer programming (MIP) model. The block pairs between which yard Cranes will be transferred during the various periods, are determined by this model. Afterwards, the model is coded by LINGO which uses branch and bound algorithm for solving. The results determine the yard Crane movement sequences among the blocks to achieve minimum total travel time for Cranes and minimum total delayed workload in blocks at different periods. The biggest portion of the objective function value, for example, with 13 blocks, eight of which lack carne capacity is related to the total travel time of the Cranes within the blocks. It is caused by the slow motion of the yard Cranes and their large size. Also, total delayed workload calculated for four Problems, decreases over the six time periods until it becomes zero. In other words, yard Cranes are deployed in an optimal manner. Also, the results show the efficiency of the developed program.

Yard Crane is an important resource in container terminals. Efficient utilization of the yard Crane significantly improves the productivity and the profitability of the container terminal. This paper presents a mixed integer programming model for the yard Crane Scheduling Problem with non- interference constraint that is NPHARD in nature. In other words, one of the most important constraints in this model which we can mention to yard Crane non- interference constraint is that they usually move on the same rails in the yard block. Optimization methods, like branch and bound algorithm, has no sufficient efficiency to solve this model and become perfectly useless when the Problem size increases. In this situation, using an advanced search method like genetic algorithm (GA) may be suitable. In this paper, a GA is proposed to obtain near optimal solutions. The GA is run by MATLAB 7.0 and the researchers used LINGO software which benefits from the Branch and Bound algorithm for comparing outputs of GA and the exact solution. We should consider the abilities of the LINGO software which is not capable of solving the Problems larger than 5 slots to 3 yard Cranes. The computational results show that the proposed GA is effective and efficient in solving the considered yard Crane Scheduling Problem.

Over the past two decades, maritime transportation and container traffic worldwide has experienced rapid and continuous growth. With the increase in maritime transportation volume, the issue of greenhouse gas (GHG) emission has become one of the new concerns for port managers. Port managers and government agencies for sustainable development of maritime transportation considered "green ports" to balance between environmental impacts and economic interests. Therefore, this study aims to integrate the Berth Allocation and Quay Crane Assignment Problem (BACAP) with speed optimization and vessels emission considerations. Rajaee port, the most important port in Iran, was selected as the case study. A mathematical model is developed based on the main characteristics of this port and is solved by GAMS IDE/CPLEX software. Given the NP-hard complexity of the BACAP, exact solution approaches need huge time, even for small and medium Problems. Hence, an adapted Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) and a Multi-Objective Simulated Annealing (MOSA) algorithm are adopted to deal with the complexity of the proposed model. Sensitivity analysis is used to assess the applicability of the proposed model and evaluate the efficiency of the solution algorithms.