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Maritime transport is the backbone of international trade of goods. Therefore, seaports are of great importance for maritime transport. Container transport plays an important role in maritime transport and is increasing year by year. Containers transported to a container terminal are stored in container yards side by side and on top of each other, forming blocks. If a container that is not on top of the block has to be retrieved, the containers that are above the required container must be relocated before the required container is retrieved. These additional container relocations, which block the retrieval of the required container, slow down the entire retrieval process. The container relocation problem, also known as the block relocation problem, is an optimization problem that involves finding an optimal sequence of operations for retrieving blocks (containers) from a container yard in a given order, minimizing additional relocations of blocking containers. In this paper, the focus is on the two-dimensional, static, offline and the restricted container relocation problem of real-size yard container bays. A new method for resolving the container relocation problem that uses a genetic algorithm is proposed to minimize the number of relocations within the bay. The method is evaluated on well-known test instances, and the obtained results are compared with the results of various relevant models for resolving the container relocation problem. The results show that the proposed method achieves the best or the second-best result for each test instance within the test set. [ABSTRACT FROM AUTHOR]

With the normalization of the global epidemic, shipping has gradually resumed, resulting in a surge in port throughput. Terminal managers are having great difficulty in making container ports be stable, orderly, and efficient. For small and medium-sized ports, making full use of the tidal water level to increase the operation time of large ships significantly improves the efficiency of port operations. Therefore, considering the impact of tidal factors on the operation of container ports, this paper firstly proposes a new berths and quay cranes allocation optimization model, T-B&QC, that minimizes the distance between the actual berthing berth and the preferred berth, the port time cost of ships and the number of quay crane movements as the optimization objectives, under constraints that consider tidal factors. Then, to solve the T-B&QC model using chaotic mapping and quantum theory, the Whale Optimization Algorithm (WOA) is integrated by using the Chaotic Quantum Rotating Gate Algorithm (CQRGA) and the Quantum Not Gate Algorithm (QNGA), whale coding rules are designed, and the Feasible-Integer Processing Algorithm (WF-IP) is established. Afterwards, the Chaotic Quantum Whale Optimization Algorithm, CQWOA, is proposed. Finally, the CQWOA is used to develop a new berth and quay crane allocation optimization method, T-B&QC_CQWOA. Subsequently, data from an actual seaport container port is used to test the reliability and superiority of the proposed distributing approach and the established optimizing algorithm. Numerical results demonstrate that the proposed distributing approach outperforms the classical distribution models that are selected herein, and the CQWOA yields a coordinated schedule of higher quality than the others in the process of solving the model. • A new berths and quay cranes allocation optimization model is modeled to satisfy the distribution plan. • This paper applies chaos mapping and quantum theory to improve the nonlinear convergence factor. • This paper designs the solution process of the whale feasible integer algorithm based on the chaotic and quantum theory. [ABSTRACT FROM AUTHOR]

The container relocation problem (CRP) is an NP-hard combinatorial optimisation problem that arises in yard management. The problem is concerned with loading all containers from the storage yard to the ship in a certain order. The yard layout consists of bays where containers are placed in stacks on top of each other, and each container has a due date that determines their retrieval order. Due to its complexity, heuristic methods are used to solve CRP, ranging from relocation rules to metaheuristics. Relocation rules (RRs) are used when the goal is to obtain a solution of acceptable quality in short time. Manually designing RRs is difficult and time-consuming, which motivates the use of different methods to automatically design RRs. In this study, we investigate the application of genetic programming (GP) to design RRs for CRP with multiple bays and container groups. The GP algorithm was adapted for generating RRs by proposing a new set of terminals and several solution construction methods. The proposed method was evaluated on an extensive benchmark of existing problems. The results obtained with automatically developed RRs were compared with the results of manually designed RRs and it was found that the automatically designed RRs performed significantly better in all cases. • Non standard problems are more difficult to solve with manual relocation rules. • Genetic programming can automatically generate new relocation rules. • Better results obtained as the number of duplicate containers increases. • Crane operation time is a pivotal criterion in multibay problems. • Automatically designed rules outperform manual rules by up to 7%. [ABSTRACT FROM AUTHOR]

Marine container terminals (MCTs) play a crucial role in intelligent maritime transportation (IMT) systems. Since the number of containers handled by MCTs has been increasing over the years, there is a need for developing effective and efficient approaches to enhance the productivity of IMT systems. The berth allocation problem (BAP) and the quay crane allocation problem (QCAP) are two well‐known optimization problems in seaside operations of MCTs. The primary aim is to minimize the vessel service cost and maximize the performance of MCTs by optimally allocating berths and quay cranes to arriving vessels subject to practical constraints. This study presents an in‐depth review of computational intelligence (CI) approaches developed to enhance the performance of MCTs. First, an introduction to MCTs and their key operations is presented, primarily focusing on seaside operations. A detailed overview of recent CI methods and solutions developed for the BAP is presented, considering various berthing layouts. Subsequently, a review of solutions related to the QCAP is presented. The datasets used in the current literature are also discussed, enabling future researchers to identify appropriate datasets to use in their work. Eventually, a detailed discussion is presented to highlight key opportunities along with foreseeable future challenges in the area. [ABSTRACT FROM AUTHOR]

The growth in containerized shipping has led to the expansion of seaports, resulting in the emergence of multiple terminals. While multi-terminal systems increase port capacity, they also pose significant challenges to container transportation, particularly in inter-terminal movements. Consequently, the transportation delay of containers in inter-terminal operations demands crucial attention, as it can adversely affect the efficiency and service levels of seaports. To minimize the total transportation delays of the inter-terminal truck routing problem (ITTRP), we introduce simulated annealing with normalized acceptance rate (SANE). SANE improves the exploration capability of simulated annealing (SA) by dynamic rescaling of the transportation delay objective to modify the acceptance probability. To validate the quality of solutions provided by SANE, we have developed a mathematical model that provides a set of linear formulations for ITTRP constraints, avoiding the known set-partitioning alternative. Experimental results showed that for small-scale ITTRP instances, SANE achieved a solution close to the optimal. In larger instances with 100–120 orders, SANE found feasible suboptimal solutions within 15–21 seconds, which is unattainable using the exact solver. Further comparison with baselines indicates that SANE provides considerable improvements compared to both SA and Tabu search in terms of the objective value. [ABSTRACT FROM AUTHOR]

Due to the rapid increase in cargoes and postal transport volumes in smart transportation systems, an efficient automated multidimensional terminal with autonomous elevating transfer vehicles (ETVs) should be established, and an effective cooperative scheduling strategy for vehicles needs to be designed for improving cargo handling efficiency. In this paper, as one of the most effective artificial intelligence technologies, the artificial bee colony algorithm (ABC), which possesses a strong global optimization ability and fewer parameters, is firstly introduced to simultaneously manage the autonomous ETVs and assign the corresponding entrances and exits. Moreover, as ABC has the disadvantage of slow convergence rate, a novel full-dimensional search strategy with parallelization (PfdABC) and a random multidimensional search strategy (RmdABC) are incorporated in the framework of ABC to increase the convergence speed. After being evaluated on benchmark functions, it is applied to solve the combinatorial optimization problem with multiple tasks and multiple entrances and exits in the terminal. The simulations show that the proposed algorithms can achieve a much more desired performance than the traditional artificial bee colony algorithm in terms of balancing the exploitation and exploration abilities, especially when dealing with the cooperative control and scheduling problems. [ABSTRACT FROM AUTHOR]

Marine container terminals play a significant role for international trade networks and global market. To cope with the rapid and steady growth of the seaborne trade market, marine container terminal operators must address the operational challenges with appropriate analytical methods to meet the needs of the market. The berth allocation and scheduling problem is one of the important decisions faced by operators during operations planning. The optimization of a berth schedule is strongly associated with the allocation of spatial and temporal resources. An optimal and robust berth schedule remarkably improves the productivity and competitiveness of a seaport. A significant number of berth allocation and scheduling studies have been conducted over the last years. Thus, there is an existing need for a comprehensive and critical literature survey to analyze the state-of-the-art research progress, developing tendencies, current shortcomings, and potential future research directions. Therefore, this study thoroughly selected scientific manuscripts dedicated to the berth allocation and scheduling problem. The identified studies were categorized based on spatial attributes, including discrete, continuous, and hybrid berth allocation and scheduling problems. A detailed review was performed for the identified study categories. A representative mathematical formulation for each category was presented along with a detailed summary of various considerations and characteristics of every study. A specific emphasis was given to the solution methods adopted. The current research shortcomings and important research needs were outlined based on the review of the state-of-the-art. This study was conducted with the expectation of assisting the scientific community and relevant stakeholders with berth allocation and scheduling. [ABSTRACT FROM AUTHOR]

Site stacking of the prefabricated building components (PBC) should be planned in coordination with installation schedule and other dynamic constraints to improve the overall construction efficiency. This study explores the collaborative planning for stacking and installation of the PBCs (CPSIP) by avoiding collision of the tower cranes that are in the overlapping area and operated simultaneously with the objective of minimizing the prefabricated construction duration. The CPSIP problem, crane-collision cases, collision-avoiding strategy, and multiphase dynamic optimization strategy are analyzed. Then the optimization model for the CPSIP problem subject to the crane-collision avoiding and other dynamic constraints is proposed, and the binary particle swarm optimization (PSO) algorithm is utilized to solve the optimization model for the CPSIP problem. A case study is presented to demonstrate and justify the developed method for obtaining the CPSIP. This study contributes to the knowledge and methodology for achieving the CPSIP by avoiding crane-collision, thus improving efficiency of the prefabricated construction while promoting safety. [ABSTRACT FROM AUTHOR]

In seaports, low-carbon energy systems and energy efficiency have become increasingly important as a result of the evolution of environmental and climate change challenges. In order to ensure the continued success of seaports, technological advancements must be introduced to a number of systems, such as seaport vehicles, harbor cranes, and the power sources of berthed ships. Harbor areas might need a microgrid to handle these aspects. Typically, microgrids that substitute conventional generator units with renewable energy sources (RES) suffer from system inertia problems, which adversely affect microgrid frequency stability. A load frequency controller (LFC) based on a novel modified proportional integral derivative with filter (MPIDF) is presented in this paper for enhancing the performance of marine microgrid system (MMS). The serval optimization algorithm (SOA), a recent bio-inspired optimization algorithm, is used to optimize the MPIDF controller coefficients. This controller is tested on a marine microgrid containing a number of RES such as wind turbine generators, sea wave energy, and solar generation. The efficacy of the proposed MPIDF controller is verified with respect to other controllers such as PIDF and PI. Similarly, the proposed meta-heuristic algorithm is validated as compared to other algorithms including particle swarm optimization (PSO), ant colony optimization (ACO), and jellyfish swarm optimization (JSO). This study also evaluates the robustness of the proposed controller to different perturbations in step load, changes in system parameters, and other parameter variations. [ABSTRACT FROM AUTHOR]

The baseline plan of terminals will be impacted to a certain extent after being affected by uncertain events, such as vessel delay and unscheduled vessel arrival, resulting in disorderly terminal operations, wasted resources, and reduced loading and unloading efficiency, which further aggravates terminal congestion. To effectively cope with the disturbance of terminal operations by the above uncertain events and improve the operational efficiency of container terminals, this paper investigates the integrated rescheduling problem of berth allocation and quay crane assignment with vessel delay and unscheduled vessel arrival. Two steps are designed to deal with uncertainty shocks. The first step is to determine the rescheduling moment by using a rolling time-domain approach. The second step is to establish a rescheduling model and design an improved genetic algorithm(IGA) to obtain a rescheduling solution using various rescheduling strategies at the rescheduling moment. Moreover, through scenario experiments, comparisons with commercial solvers and other algorithms, it can be seen that the solution speed of IGA is better than that of commercial solvers and the average gap does not exceed 6%, which verifies the effectiveness and superiority of this algorithm. [ABSTRACT FROM AUTHOR]

With the gradual increase of commercial banks and the expansion of their branches, the demand for cash transportation inflates sharply, bringing opportunities to the business development of Cash-In-Transit (CIT) sectors. However, the branches are often distributed in densely populated areas where traffic jams occur from time to time, which poses a severe challenge to the route planning of CIT vehicles. In addition, risk factors need to be considered during the optimization process because the goods transported belong to valuables. In order to effectively deal with the routing problem of CIT sectors, this paper established a bi-objective model and a goal programming model of Risk-Constrained Multi Depot Vehicle Routing Problems (RCMDVRPs) using real-time traffic data. Based on the traditional genetic algorithm, a Hybrid Genetic Algorithm with Intensification procedures (HGAI) is proposed to solve the goal programming model by using a three-level linked list structure to express chromosomes visually. Then, a new Self-constrained Hybrid Genetic Algorithm (SHGA) is designed for the bi-objective model. Besides, an online path updating strategy is developed to guide remote vehicles against time-dependent traffic flows. Finally, the HGAI is performed on benchmark instances to verify its accuracy. Experimental results of performance test show that the algorithm can achieve a gap of about 3% compared with the Best Known Result (BKR). The results of a case study also show that the two models and the corresponding algorithms are feasible and can be used to solve large-scale problems according to the special preferences and goals of decision-makers. [ABSTRACT FROM AUTHOR]

Background: In global trade, shipping companies are forced to manage empty containers due to imbalances in international trade activities. For decision-makers, the problems require considering restrictions and an uncertain environment and repositioning or leasing the containers to satisfy the rapidly changing global demands regardless of the epidemic outbreak's impact on the seaport. The proposed approach can help decision-makers manage the empty container in port yards more effectively under market uncertainty by employing the Bellman optimality principle for the stochastic dynamic system. Methods: A stochastic production planning model is employed to cope with uncertainty and unexpected events to ensure a robust management strategy. Ito's formula describes the dynamic model for solving a stochastic differential equation. This paper uses stochastic optimal control theory to deal with efficient empty container management at the port yard. The findings have revealed the effectiveness of the proposed framework, which will provide a decision-making support scheme for efficient port operations. Results: The presented algorithm is realized by a novel approach, employing the Hamilton-Jacobi-Bellman (HJB) equation for optimal stochastic control problems. When comparing the model with and without uncertainty events, the gap is just about 0.04 %, proving the robustness of the proposed model. The results provide a decision support system for port managers when managing the empty container in the seaport yard. Conclusions: The proposed model not only figures out the optimal ordering of empty containers for each cycle but also points out the optimal safety stock level. Using a stochastic optimization approach, decision-makers can implement a strategic management policy to optimize seaport operational costs under market disruptions. [ABSTRACT FROM AUTHOR]

Each shipping line is expected to establish a reliable operating model, and the design of ship schedules is a key operational consideration. Long-term profits for shipping lines can be expected from a well-designed ship schedule. In today's liner service design, managing the time factor is critical. Shipping schedules are prone to different unexpected disruptions. Such disruptions would necessitate a near-real-time analysis of port capacity and re-design of the original ship schedule to offset the negative externalities. Ship schedule recovery strategies should be implemented to mitigate the effects caused by disruptions at ports or at sea, which may include, but are not limited to, ship sailing speed adjustment, handling rate adjustment at ports, port skipping, and port skipping with container diversion. A proper selection of ship schedule recovery strategies is expected to minimize deviations from the original ship schedule and reduce delays in the delivery of cargoes to the destination ports. This article offers a thorough review of the current liner shipping research primarily focusing on two major themes: (1) uncertainties in liner shipping operations; and (2) ship schedule recovery in response to disruptive events. On the basis of a detailed review of the available literature, the obtained results are carefully investigated, and limitations in the current state-of-the-art are determined for every group of studies. Furthermore, representative mathematical models are provided that could be further used in future research efforts dealing with uncertainties in liner shipping and ship schedule recovery. Last but not least, a few prospective research avenues are suggested for further investigation. [ABSTRACT FROM AUTHOR]

Container terminals play a crucial role in exporting and importing goods, where export and import containers are loaded and unloaded. Containers are usually loaded and unloaded with a dock crane. A quay crane is assigned at a container port in advance, considering a ship's arrival schedule. However, allocating a quay crane is difficult owing to the limited number of quay cranes available and the need to consider the shipping timetable. In this study, by considering the schedule of each ship arriving from a container terminal, the number of unloaded containers for each ship, and the limited number of quay cranes, we conduct quay crane assignment through a simulation model to increase the productivity of a container terminal. Alongside, it is evident that artificial intelligence (AI) and machine learning (ML) are necessary for port management in many ways, from berth scheduling to quay allocation. In this study, we also investigate the applicability of ML and metaheuristic approaches in quay allocation problems and explore further possibilities. In this study, we also highlight the sensor-based automation of quay allocation using Internet of Things (IoT) technologies for future research in the domain of port and terminal management. [ABSTRACT FROM AUTHOR]

Automated container terminals (ACTs) represent state-of-the-art facilities for container handling and are a current development trend. However, enhancing their operational efficiency while minimizing energy consumption remains a challenge. While metaheuristics are helpful in addressing container terminal problems, their capabilities are limited when used alone to tackle complex or integrated problems. Hybrid models can be more effective in facing these challenges. This research proposes a hybrid model, termed WOA + PSO, which combines the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) as a novel approach to address the integrated scheduling problem of automated quay cranes (AQCs), automated lift vehicles (ALVs), and automated stacking cranes (ASCs) in an ACT. Additionally, the WOA + PSO collaborates with a simulation model in a framework to become a simulation-based optimization approach. The performance of WOA + PSO is evaluated by comparing it with its base models, WOA and PSO, as well as a genetic algorithm (GA), through extensive experiments. The results show that WOA + PSO outperforms the others in achieving the objective of balancing operational efficiency and energy consumption. [ABSTRACT FROM AUTHOR]

Within the context of using shore side electricity (SSE) at container terminals, we propose a multi-objective optimization model to solve the problem of berth allocation and quay crane assignment (BACAP). The proposed optimization model integrates the interconnected decisions on each vessel's berthing position, berthing start and departure time, duration of using SSE, and duration of using auxiliary engines to minimize the costs of using SSE, departure delay and emissions. Several factors, including the availability of SSE at different berths and vessels and the time-of-use (TOU) electricity pricing, which have been frequently ignored by previous studies, are specifically considered in this paper. Due to the complexity of the proposed model, an integrative algorithm framework is developed, composed of Partial Optimization Metaheuristic Under Special Intensification Conditions (POPMUSIC), Strength Pareto Evolutionary Algorithm 2 (SPEA2), and k -means clustering. The numerical experiments for a real-world case illustrate the efficiency of the developed algorithm framework and the effectiveness of the proposed model. Compared to the best-known solutions with only one objective considered, the proposed model reduces the costs of using SSE, departure delay, and vessel emissions by 27.47%, 37.51%, and 51.44%. Besides, some managerial insights are outlined based on the experiments under a series of designed scenarios. In doing so, the proposed optimization model assists the promotion of using SSE and the development of green ports and green shipping. • Proposing a multi-objective optimization model for the quayside operation. • Considering the availability of shore side electricity at berths and vessels. • Considering the impact of time-of-use electricity pricing. • Developing an integrative algorithm framework. • Performing experiments on well-defined instances generated from real-word data. [ABSTRACT FROM AUTHOR]