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 (BA(QCAP)) 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.

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 (BA(QCAP)) 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.

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.

Ports are important hubs that connect maritime transport to other modes. For this reason, ports are considered important infrastructures for freight transportation, trade, and the global economy. Therefore, optimizing port plans and operations will play a significant role in port costs and revenues. Berth planning is part of port operations that includes two Problems; Berth Allocation Problem (BAP) and Quay Crane Assignment Problem ((QCAP)). The optimal solution to these two Problems can affect the freight transportation speed and cost. This paper integrated modeling two Problems BAP and (QCAP), and solved them using four metaheuristic algorithms (GA, ICA, TS, ACO). Whereas berth planning is in a disrupted environment and unforeseen events may occur during the program's execution, incurring additional costs for the port, or even stopping the initial plan execution, rescheduling the original berth plan is necessary. This paper uses two methods (global and local rescheduling) with metaheuristic algorithms to find the optimal method for berth rescheduling. Numerical analysis shows the proper performance of GA and ICA algorithms in solving the initial berth planning model and the global rescheduling method. In contrast, for local rescheduling, the ACO algorithm has better performance. Evaluation and validation of models and methods of solving this paper are done using the data of ships' arrival and departure in Shahid Rajaei port.

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.

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.

Objectives: This study was conducted to investigate psychometric characteristics of Quay-Peterson Revised Behavior Problem Checklist. Method: The core of this study was scale development and 458 schoolchildren (255 boys and 203 girls) from elementary schools, of four educational districts in the city of Shiraz (Iran) were recruited using cluster random sampling method. The subjects were then assessed using Quay-Peterson Revised Behavior Problem Checklist. Results: The validity of the checklist was obtained using item-total correlations, factor analysis and the correlation between the checklist scores and Rutter Children Behavior Questionnaire total score (0.88). Factor analysis using principle component analysis followed by varimax rotation led to the extraction of four factors with eigenvalues of greater than one which included: conduct disorder, attention Problems-immaturity, social aggression and anxiety-withdrawal. The reliability of the scale using test-retest was as follows: 0.88 for total score, 0.90 for conduct disorders, 0.85 for attention Problems- immaturity, 0.79 for social aggression, and 0.78 for anxiety-withdrawal. Cronbach alpha coefficients were 0.98, 0.97, 0.97, 0.82, and 0.85 for the total score, conduct disorder, attention Problems-immaturity, social aggression and anxiety-withdrawal respectively. Conclusion: Quay-Peterson Revised Behavior Problem Checklist is a valid and reliable instrument for use in Iran.