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The strategic optimization of a container terminal's quayside assets, including the berth and quay cranes, is crucial for maximizing their deployment and utilization. The interrelated and complex challenges of Berth Allocation (BAP) and Quay Crane Scheduling (QCSP) are fundamental to enhancing the resilience of container ports, as berths and quay cranes constitute essential infrastructure. Efficient berth allocation and quay crane scheduling can mitigate operational disruptions, even in the face of maintenance or failures, thereby improving both operational reliability and resilience. However, previous studies have often overlooked the uncertainty associated with quay crane maintenance when planning these operations. This paper aims to minimize vessel turnaround time by accounting for the uncertain in quay crane maintenance activities. To address this novel problem, we propose a proactive-reactive method that incorporates a reliability-based model into the Swarm Optimization with Differential Evolution (SWO-DE) algorithm. Computational results confirm the practical relevance and effectiveness of our proposed solution methods for container terminals. [ABSTRACT FROM AUTHOR]

The International Maritime Organization (IMO) aims for net zero emissions in shipping by 2050. Ports, key links in the supply chain, are embracing green innovation, focusing on efficient berth and quay crane scheduling to support green port development amid limited resources. Additionally, the energy consumption and carbon emissions from the port shipping industry contribute significantly to environmental challenges and the sustainable development of ports. Therefore, reducing carbon emissions, particularly those generated during vessel berthing, has become a pressing task for the industry. The increasing complexity of berth allocation now requires compliance to vessel service standards while controlling carbon emissions. This study presents an integrated model that incorporates tidal factors into the joint optimization of berth and quay crane operations, addressing both service standards and emissions during port stays and crane activities, and further designs a PSO-GA hybrid algorithm, combining particle swarm optimization (PSO) with crossover and mutation operators from a genetic algorithm (GA), to enhance optimization accuracy and efficiency. Numerical experiments using actual data from a container terminal demonstrate the effectiveness and superiority of the PSO-GA algorithm compared to the traditional GA and PSO. The results show a reduction in total operational costs by 24.1% and carbon emissions by 15.3%, highlighting significant potential savings and environmental benefits for port operators. Furthermore, the findings reveal the critical role of tidal factors in improving berth and quay crane scheduling. The results provide decision-making support for the efficient operation and carbon emission control of green ports. [ABSTRACT FROM AUTHOR]

Effective berth allocation in container terminals is crucial for optimizing port operations, given the limited space and the increasing volume of container traffic. This study addresses the discrete dynamic berth allocation problem (DDBAP) under uncertain ship arrival times and varying load capacities. A novel deep Q-network (DQN)-based model is proposed, leveraging a custom state space, rule-based actions, and an optimized reward function to dynamically allocate berths and schedule vessel arrivals. Comparative experiments were conducted with traditional algorithms, including ant colony optimization (ACO), parallel ant colony optimization (PACO), and ant colony optimization combined with genetic algorithm (ACOGA). The results show that DQN outperforms these methods significantly, achieving superior efficiency and effectiveness, particularly under high variability in ship arrivals and load conditions. Specifically, the DQN model reduced the total waiting time of vessels by 58.3% compared to ACO (262.85 h), by 57.9% compared to PACO (259.5 h), and by 57.4% compared to ACOGA (257.4 h), with a total waiting time of 109.45 h. Despite its impressive performance, DQN requires substantial computational power during the training phase and is sensitive to data quality. These findings underscore the potential of reinforcement learning to optimize berth allocation under dynamic conditions. Future work will explore multi-agent reinforcement learning (MARL) and real-time adaptive mechanisms to further enhance the robustness and scalability of the model. [ABSTRACT FROM AUTHOR]

Marine transportation has become a vital element of global trade, connecting commercial hubs around the world via low-cost sea routes. Its impact is increased by the environmental concerns raised by the associated maritime traffic, which necessitates a comprehensive and efficient method to resolving these worries. A vessel follows a predefined course and departs from the home port on a scheduled basis in order to reach its destination. It carries out loading and unloading operations at the allocated berth and crane during the tour. In order to conserve schedule, the vessel needs to navigate the route at the optimal speed, which is influenced by a number of factors including fuel consumption and vessel weight. This study used a novel model to generate a vessel schedule and route map for Iran's Shahid Rajaei Port in the Persian Gulf. The data suggest that the port can manage ten vessels at a time and has two cranes for loading and unloading each vessel. In addition, we carried out a sensitivity analysis on key components of our proposed model, including fuel costs, vessel weight, load-carrying capacity, and arrival/departure delays. The keys findings are as: higher arrival/departure costs result in shorter delays; higher fuel costs have a negative impact on the objective function; lower vessel weight results in better fuel efficiency; and higher vessel load-carrying capacity is coupled with higher fuel costs. [ABSTRACT FROM AUTHOR]

This paper presents a chaotic optimal thermodynamic evolutionary algorithm (COTEA) designed to address the integrated scheduling problems of berth allocation, ship unloader scheduling, and yard allocation at bulk cargo terminals. Our proposed COTEA introduces a thermal transition crossover method that effectively circumvents local optima in the scheduling solution process. Additionally, the method innovatively combines a good point set with chaotic dynamics within an integrated initialization framework, thereby cultivating a robust and exploratory initial population for the optimization algorithm. To further enhance the selection process, our paper proposes a refined parental selection protocol that employs a quantified hypervolume contribution metric to discern superior candidate solutions. Postevolution, our algorithm employs a Cauchy inverse cumulative distribution-based neighborhood search to effectively explore and enhance the solution spaces, significantly accelerating the convergence speed during the scheduling solution process. The proposed method is adept at achieving multiobjective optimization, simultaneously improving the service level and reducing costs for bulk cargo terminals, which in turn boosts their competitiveness. The effectiveness of our COTEA is demonstrated through extensive numerical simulations. [ABSTRACT FROM AUTHOR]

Efficient coordinated scheduling has long been a focal point in port research, complicated by the diverse optimization goals dictated by different port characteristics. This study focuses on Yangtze River ports, exploring coordinated scheduling amidst river–sea intermodal transportation. Our research aims to reduce berth deviation costs and shorten the total scheduling time for ships, while maximizing berth utilization rates for ports. Initially, we analyzed the operational realities of Yangtze River ports and waterways. Subsequently, we innovatively introduced three key factors influencing scheduling: berth preferences, seagoing ship inspections, and planning cycles. Finally we proposed the optimized Non-dominated Sorting Genetic Algorithm III (NSGA-III). Evaluating the model using a seven-day dataset of vessel activities at Yangtze River ports revealed significant improvements: the optimized NSGA-III enhanced objective values by 30.81%, 13.73%, and 12.11% compared to the original scheduling approach, surpassing both conventional NSGA-III and NSGA-II algorithms. This study underscores the model's efficacy in not only reducing operational costs through optimized ship and berth sequencing but also in enhancing clearance efficiency for relevant authorities. [ABSTRACT FROM AUTHOR]

The International Maritime Organization (IMO) aims for net zero emissions in shipping by 2050. Ports, key links in the supply chain, are embracing green innovation, focusing on efficient berth and quay crane scheduling to support green port development amid limited resources. Additionally, the energy consumption and carbon emissions from the port shipping industry contribute significantly to environmental challenges and the sustainable development of ports. Therefore, reducing carbon emissions, particularly those generated during vessel berthing, has become a pressing task for the industry. The increasing complexity of berth allocation now requires compliance to vessel service standards while controlling carbon emissions. This study presents an integrated model that incorporates tidal factors into the joint optimization of berth and quay crane operations, addressing both service standards and emissions during port stays and crane activities, and further designs a PSO-GA hybrid algorithm, combining particle swarm optimization (PSO) with crossover and mutation operators from a genetic algorithm (GA), to enhance optimization accuracy and efficiency. Numerical experiments using actual data from a container terminal demonstrate the effectiveness and superiority of the PSO-GA algorithm compared to the traditional GA and PSO. The results show a reduction in total operational costs by 24.1% and carbon emissions by 15.3%, highlighting significant potential savings and environmental benefits for port operators. Furthermore, the findings reveal the critical role of tidal factors in improving berth and quay crane scheduling. The results provide decision-making support for the efficient operation and carbon emission control of green ports.

The strategic optimization of a container terminal’s quayside assets, including the berth and quay cranes, is crucial for maximizing their deployment and utilization. The interrelated and complex challenges of Berth Allocation (BAP) and Quay Crane Scheduling (QCSP) are fundamental to enhancing the resilience of container ports, as berths and quay cranes constitute essential infrastructure. Efficient berth allocation and quay crane scheduling can mitigate operational disruptions, even in the face of maintenance or failures, thereby improving both operational reliability and resilience. However, previous studies have often overlooked the uncertainty associated with quay crane maintenance when planning these operations. This paper aims to minimize vessel turnaround time by accounting for the uncertain in quay crane maintenance activities. To address this novel problem, we propose a proactive-reactive method that incorporates a reliability-based model into the Swarm Optimization with Differential Evolution (SWO-DE) algorithm. Computational results confirm the practical relevance and effectiveness of our proposed solution methods for container terminals.

In order to solve the problem of parking space allocation and pricing in shared parking space management, considered the influence of price changes on users' choice of different parking duration was, the probability of users' choice of parking duration under different parking charging standards was analyzed, and a refined price elasticity function of shared parking demand by polynomial fitting was obtained. Furthermore, in order to realize the dynamic adjustment of parking lot price and parking space allocation, a MINLP (mixed integer nonlinear programming) model of parking space allocation based on elastic demand and dynamic pricing was constructed with the goal of maximizing platform revenue and minimizing user walking distance, and a genetic algorithm was designed. Finally, this paper designs an example that includes 3 parking lots with a total of 1 000 berths, and the operation time of the shared platform is 3 h, and verifies the model and algorithm. Through comparative analysis, compared with static pricing, the shared berth allocation scheme based on elastic demand and dynamic pricing achieves a 19% increase in platform revenue and meets most parking demands, which shows the effectiveness of the shared berth allocation model and algorithm under dynamic pricing, and provides a new idea for parking management of the shared platform. [ABSTRACT FROM AUTHOR]

Maritime shipment plays a pivotal role in goods transportation worldwide. Ships intending to load/unload a cargo should visit a port. Three main steps are taken during this process. Firstly, ships go through a waterway to reach the berthing area. Secondly, they are allocated to eligible wharves, and quay cranes are assigned to them. At last, ships leave the port area through the waterway. In this paper, the three mentioned stages are considered and solved in an integrated manner. A two-way waterway is deemed with the width limitation for ships moving in opposite directions. Furthermore, ships may arrive during the planning horizon. Tidal impacts, draft limit, and safety distance between ships are prominent maritime limitations included in this paper. As the three stages take place consecutively, the problem appears to resemble a hybrid flow shop scheduling problem with unrelated parallel machines, machine eligibility constraint, and shared resources. Following this attitude, a mathematical model is developed. Due to the problem complexity, three distinct matheuristic approaches are developed by combining the genetic algorithm, discrete differential evolution, and grey wolf optimiser with a mathematical model and tested using randomly-generated numerical instances. At last, developed matheuristics are compared, and some managerial insights are provided. [ABSTRACT FROM AUTHOR]

Efficient coordinated scheduling has long been a focal point in port research, complicated by the diverse optimization goals dictated by different port characteristics. This study focuses on Yangtze River ports, exploring coordinated scheduling amidst river–sea intermodal transportation. Our research aims to reduce berth deviation costs and shorten the total scheduling time for ships, while maximizing berth utilization rates for ports. Initially, we analyzed the operational realities of Yangtze River ports and waterways. Subsequently, we innovatively introduced three key factors influencing scheduling: berth preferences, seagoing ship inspections, and planning cycles. Finally we proposed the optimized Non-dominated Sorting Genetic Algorithm III (NSGA-III). Evaluating the model using a seven-day dataset of vessel activities at Yangtze River ports revealed significant improvements: the optimized NSGA-III enhanced objective values by 30.81%, 13.73%, and 12.11% compared to the original scheduling approach, surpassing both conventional NSGA-III and NSGA-II algorithms. This study underscores the model’s efficacy in not only reducing operational costs through optimized ship and berth sequencing but also in enhancing clearance efficiency for relevant authorities.

The green and sustainable development of ports is an issue of great concern to governments, industries, and academia. Carbon emissions near ports mainly come from ships and trucks. The effective allocation of resources and the implementation of a carbon tax policy may reduce carbon emissions near ports. Therefore, this paper investigates an integrated optimization problem of berths, quay cranes, and yard spaces allocation for tidal ports with channel capacity constraints under carbon tax policy. A mathematical model that aims to minimize the sum of the total carbon emission costs and total penalty costs is proposed. To solve the model efficiently, a heuristic algorithm based on the sequential method is developed. Numerical experiments are performed to verify the efficiency of the proposed algorithm. Based on the results of numerical experiments, some potentially useful managerial implications are outlined. [ABSTRACT FROM AUTHOR]

Berth allocation is a critical concern in container terminal port logistics, involving the precise determination of where and when arriving vessels should dock along a quay. With berth space limitations and a continuous surge in container handling demands, ensuring an effective berth allocation is paramount for the smooth and efficient operation of container ports. However, due to the randomness of vessel arrival times and uncertainties surrounding container ship loading capacities, berth allocation problems (BAP) often present discrete and dynamic challenges. This paper addresses these challenges by considering real-world terminal operational factors, formulating relevant assumptions, and establishing a model for dynamic berth allocation and efficient ship berthing scheduling. The primary motivation stems from the parallels observed between the BAP problem and ant foraging path selection, leading to the proposal of a novel Parallel Search Structure Enhanced Ant Colony Algorithm (PACO). A proper set of parameters of the algorithm are selected based upon sensitivity analyses on the convergence and parallelism efficiency of the algorithm. To validate our method, a real-world case-container terminal operation in Shanghai Port was studied. The experimental comparison results show that the PACO algorithm outperforms other commonly used algorithms, making it more effective and efficient for the Discrete Dynamic Berth Allocation Problem (DDBAP). [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]

With the rapid development of worldwide container trade, quay cranes and other resources in container terminals are suffering from increasing workloads. To reduce their failure probabilities, quay cranes usually require preventive maintenance. However, these important activities are often ignored during quay crane planning in literature. This work studies an integrated berth allocation, quay crane assignment and specific quay crane assignment problem where quay crane maintenance is involved, and establishes an integer linear programming with the objective of minimising the total turnaround time. Due to the NP-hardness of the problem, CPLEX only solves instances with up to 18 vessels. For solving larger instances, we propose Left-and-Right Vessel Move (LRVM) algorithm and improved Genetic Algorithm (GA). Experimental results reveal that GA slightly outperforms LRVM with respect to solution quality as the solution of LRVM serves as one of the initial chromosomes of GA. LRVM can output feasible solutions within seconds, even for instances with 50 vessels. We further present a sensitivity analysis of preferred berth segments, and make numerical comparison on algorithm performance for the relevant model without quay crane maintenance. Both the models and proposed heuristics in this work help in dealing with the operational management of container terminal resources in practice. [ABSTRACT FROM AUTHOR]

Maritime transportation plays a critical role in global trade as it accounts for over 80% of all merchandise movement. Given the growing volume of maritime freight, it is vital to have an efficient system for handling ships and cargos at ports. The current first-come-first-serve method is insufficient in maintaining operational efficiency, especially under complicated conditions such as parallel scheduling with different cargo setups. In addition, in the face of rising demand, data-driven strategies are necessary. To tackle this issue, this paper proposes a mixed-integer model for allocating quay cranes, terminals, and berths. It considers not only cargo types, but also the time required for a quay crane setup. The proposed model features a greedy-insert-based offline algorithm that optimizes berth allocation when vessel information is available. In situations where vessel information is uncertain, the model utilizes an online optimization strategy based on a reinforcement-learning algorithm that is capable of learning from feedback and of adapting quickly in real time. The results of the numerical experiments demonstrate that both the offline and online algorithms can significantly enhance cargo handling efficiency and overall harbor operation. Furthermore, they have the potential to be extended to other complex settings. [ABSTRACT FROM AUTHOR]

With the increasing volume of container freight transport, future port planning is crucial. Simulation models provide a means to gain insight in the effects of terminal expansions. Detailed simulations incorporate berth allocation: assigning vessels a time and location at the quay wall, where the vessel is loaded and unloaded. This article develops decision models for both offline preliminary berth planning and for online recovery of this plan during simulation. First, we develop an optimisation-based approach that incorporates realistic aspects—cyclic vessel arrivals, tidal windows, and minimisation of vessel draught during low water periods—in order to develop a cyclic baseline berth allocation plan. The approach can proactively incorporate slack for increased robustness. Exploiting a constraint-based solver, we can obtain optimal or satisficing solutions for a year's operation of a large port. The resulting preliminary berth plan is used as a basis for the arrival times. However, disruptions can occur, such as vessel arrival and loading times varying from the planned. Hence, second, we develop a real-time disruption management decision model. This multi-level heuristic approach reacts to disruptions while minimising perturbation of the original berth plan. Computational experiments with a high-resolution simulator show our recovery approach finds good solutions until a tipping point of disturbance. Results also show that when the expected occupation of a terminal is higher, strengthening robustness of the preliminary plan has increased importance. The approach described in the article is implemented for a major European inland tidal port, forming the basis of a simulation-based decision support tool for operational planning and exploring port expansion options. [ABSTRACT FROM AUTHOR]

Confronting the challenges provided by jumbo container vessels, managers of world-class seaports must take into consideration water depths-related constraints when making the vessel berthing decisions. That is, when the water depths satisfy the requirement of jumbo vessels' drafts, these vessels are allowed to sail through the navigation channel. In this paper, we study a tactical berth allocation problem under the consideration of time-varying water depths. For the resolution, we propose an integer programming model, a set partitioning formulation and an improved differential evolutionary algorithm. Computational experiments demonstrate that the set partitioning formulation outperforms the integer programming model and can be applied to solve small- and medium-sized problems. The improved differential evolutionary algorithm is recommended for large-sized problems. [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]

The intelligent management of available resources is one of the greatest challenges of any organization. Find the balance between the size of the stock and the production and transport capacity and ensuring quality service to suppliers and customers. This type of challenge is also very common in port terminals. Ensuring efficient and effective operations is fundamental to reduce fines, avoid accidents, and build customer loyalty. This paper considers integrated planning, scheduling, yard allocation, and berth allocation problem in dry bulk port terminals. The integrated problem consists of planning and scheduling the flow of products between the supply and demand nodes, allocating the products to the storage yards, and determining the loading sequence and berth time and position of each vessel. A mixed-integer linear programming model is proposed, connecting the problems and generating an integrated solution. To solve the integrated problem more efficiently, we developed an algorithm that combines the column generation method with a diving heuristic with limited backtracking, a relax-and-fix heuristic, and an exact algorithm from a commercial solver. The mathematical formulation and the proposed algorithm are tested and validated with large-scale instances. Computational experiments show that the proposed solution approach outperform commercial solver and is very effective in finding strong bounds for large instances. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University: Engineering Science is the property of Editorial Office of Journal of Zhengzhou University: Engineering Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Berth allocation is a critical concern in container terminal port logistics, involving the precise determination of where and when arriving vessels should dock along a quay. With berth space limitations and a continuous surge in container handling demands, ensuring an effective berth allocation is paramount for the smooth and efficient operation of container ports. However, due to the randomness of vessel arrival times and uncertainties surrounding container ship loading capacities, berth allocation problems (BAP) often present discrete and dynamic challenges. This paper addresses these challenges by considering real-world terminal operational factors, formulating relevant assumptions, and establishing a model for dynamic berth allocation and efficient ship berthing scheduling. The primary motivation stems from the parallels observed between the BAP problem and ant foraging path selection, leading to the proposal of a novel Parallel Search Structure Enhanced Ant Colony Algorithm (PACO). A proper set of parameters of the algorithm are selected based upon sensitivity analyses on the convergence and parallelism efficiency of the algorithm. To validate our method, a real-world case-container terminal operation in Shanghai Port was studied. The experimental comparison results show that the PACO algorithm outperforms other commonly used algorithms, making it more effective and efficient for the Discrete Dynamic Berth Allocation Problem (DDBAP).

This paper presents a decision support system for the core problem of berth allocation decision in a container terminal. The allocation of berths to the calling vessels is complex with the fact that different service level requirements are required for different vessels. Terminal managers demand for effective decision support systems that would aid them with the allocation problem considering service priorities. Consequently, this study provides a DSS, built by a dynamic discrete-event simulation model embedded with an optimization tool that determines the priority controls for the berth allocation to the calling vessels. To show the practical application of the DSS, a comprehensive case study from a Turkish container terminal considering the current state and future expansion plans that also provides an indication of the usability aspect of the program on other ports around the world has been conducted. Further experiments are conducted based on data from the Port of Rotterdam. The DSS presented in this study may help port authorities in determining more efficient allocation decisions within a container terminal. [ABSTRACT FROM AUTHOR]

Berth allocation problems are amongst the most important problems occurring in port terminals, and they are greatly affected by several unpredictable events. As a result, the study of these problems under uncertainty has been a target of more and more researchers. Following this research line, we consider the berth allocation problem under uncertain handling times. A distributionally robust two-stage model is presented to minimize the worst-case of the expected sum of delays with respect to a set of possible probability distributions of the handling times. The solutions of the proposed model are obtained by an exact decomposition algorithm for which several improvements are discussed. An adaptation of the proposed algorithm for the case where the assumption of relatively complete recourse fails is also presented. Extensive computational tests are reported to evaluate the effectiveness of the proposed approach and to compare the solutions obtained with those resulting from the stochastic and robust approaches. • A distributionally robust model is introduced for the berth allocation problem. • A Wasserstein distance is used to model the ambiguity set. • An exact decomposition algorithm is developed to solve the problem. • Strategies to improve the performance of the solution procedure are discussed. • Computational results show the applicability of the solution approaches. [ABSTRACT FROM AUTHOR]

Container seaport congestion is a challenging problem in improving the service level and optimizing evacuating container vessels after congestion. There is a lack of research on container vessel evacuation strategies for continuous terminals. In this article, the weight of the objective function is regarded as the index for the service priority of vessels. The effects of the service priority on the continuous terminal are analyzed by establishing a mixed integer programming model. The model minimizes the total weighted delay departure time of vessels. Two sets of weight values are adopted, including handling volume of each ship and the squared handling volume, then the optimization results are compared with the unweighted scenario. The model is solved using a genetic algorithm. Lianyungang Port is selected as a case study. The results show that the method using the square of handled container volume is more conducive to ensuring the shipping period of large vessels after congestion. Besides, the quay crane number of large vessels affecting the scheduling strategy is discussed. The method proposed in this article provides a new idea for arranging scheduling strategies in other ports under congestion situations, which can better ensure the planned shipping period of large vessels. [ABSTRACT FROM AUTHOR]

• A joint berth allocation and ship loader scheduling problem in coal export terminals. • Deballasting restrictions and symmetrical berth layout-based rotary loading mode are considered. • A mixed-integer programming model is developed to minimize ship tardiness. • A logic-based Benders decomposition algorithm and effective acceleration strategies are proposed. • Compared to practical scheduling, the proposed methods achieve a reduction of 51.9% on tardiness. Due to nonmatching between the efficiency of ship deballasting and terminal loading, operations in modern coal export terminals (CETs) are frequently interrupted and significantly restricted by ship deballasting delays. This paper addresses a novel integrated problem of berth allocation and ship loader scheduling arising in CETs with new features, including the symmetrical berth layout-based rotary loading mode (SBLRLM) and deballasting restrictions. A mixed-integer programming (MIP) model is proposed to make joint decisions to minimize the total tardiness of all ships. By exploring the problem's decomposable structure, an exact method that relies on logic-based Benders decomposition (LBBD) is proposed to obtain optimal solutions. According to the problem features, the monolithic MIP formulation is decomposed into a new MIP (master problem) and a constraint programming (subproblem). Combinatorial valid inequalities are developed to enhance the master problem model and obtain a tighter lower bound. A Benders cut strengthening heuristic is developed to generate feasibility and optimality cuts to ensure efficient algorithm convergence. To evaluate the proposed method, computational experiments are conducted on a set of test instances generated based on real data collected from a major CET in northern China. The results indicate that the LBBD algorithm can optimally solve large-scale problem instances within a reasonable amount of time. The plan generated by the LBBD algorithm achieves a 51.9% reduction in ship tardiness, on average, compared to that of practical scheduling methods. By using the proposed method, the superiority of the SBLRLM in reducing deballasting delays becomes more significant as the problem scale increases. In addition, considering different working intensities, the results could also help port operators make decisions about the number of berths in the SBLRLM. [ABSTRACT FROM AUTHOR]

• Integrated strategic- and tactical-level scheduling and operations at container ports. • Develop a two-stage stochastic integer programming model with a tailored decomposition method. • Conduct extensive numerical experiments and derive managerial insights. Improving container ports' operational efficiency to reduce the delays at ports concerns global port operators significantly. Optimizing the tactical level allocation of quay side and yard side resources to vessel calls is a typical lever to mitigate the delays in operations. Another lever is by strategical level planning that re-adjust the preferred visiting time windows of vessel calls. It aims to absorb the delays at the scheduling stage rather than in the operations stage. This paper integrates the planning and operations at container ports to jointly optimize strategical level planning and tactical level berth and yard space allocation under uncertain vessel arrival times and uncertain numbers of loading/unloading containers. The problem is formulated as a two-stage stochastic integer programming model. To solve it, we develop an original decomposition algorithm that passes columns of second-stage problems to the first-stage problem to approximate the second-stage decision making. Numerical experiments are conducted to validate the effectiveness and efficiency of our proposed algorithm. Some managerial implications for port operators are also obtained. [ABSTRACT FROM AUTHOR]

The uncoordinated berth allocation of all-electric ships (AES) will impose great challenges on voltage profiles in seaport microgrids. This paper proposes a distributed optimal voltage control and berth allocation strategy to mitigate voltage violations while balancing the benefits between microgrids and AES. The voltage control problem is formulated as a second-order cone program (SOCP) to minimize network power loss, while the berth allocation problem is formulated as a mixed-integer linear program (MILP) to minimize total service time. To build the bridge between microgrids and berthed-in AES, the power interaction model at the seaport is proposed. The proposed model introduces a time-cost factor to evaluate the economic cost of service time and also unifies the timescales between voltage control and berth allocation. Considering different system operators and objectives of microgrids and AES, this paper develops an alternating direction of multipliers (ADMM)-based distributed optimization algorithm to solve the problem. The method can help reach the convergence of ADMM in solving models with discrete variables. The proposed approach is tested on the EU 16-bus microgrid embedded with a seaport and simulation results verify the effectiveness both in mitigating voltage violations and maximizing the total benefits of microgrids and AES. [ABSTRACT FROM AUTHOR]

An Integrated Approach to Managing Vessel Service in Seaports Efficient vessel service is of utmost importance in the maritime supply chain. When serving a group of incoming vessels, berth allocation and pilotage planning are the two most important decisions made by a seaport. Although they are closely correlated, the berth allocation problem and pilotage planning problem are often solved sequentially, leading to suboptimal or even infeasible solutions for vessel services. In "Vessel Service Planning in Seaports," Wu, Adulyasak, Cordeau, and Wang focus on a vessel service planning problem that optimizes berth allocation and pilotage planning in combination. To solve the joint problem, the authors develop an exact solution method that combines Benders decomposition and column generation within an efficient branch-and-bound framework. They also propose acceleration strategies that significantly improve the performance of the algorithm. Test instances from one of the world's largest seaports are used to validate the effectiveness of the approach and demonstrate the value of integrated planning. Berth allocation and pilotage planning are the two most important decisions made by a seaport for serving incoming vessels. Traditionally, the berth allocation problem and the pilotage planning problem are solved sequentially, leading to suboptimal or even infeasible solutions for vessel services. This paper investigates a vessel service planning problem (VSPP) in seaports that addresses berth allocation and pilotage planning in combination. We introduce a compact mixed-integer linear programming formulation for the problem, which can be solved by general-purpose solvers. To solve large-scale instances, we develop an exact solution approach that combines Benders decomposition and column generation within an efficient branch-and-bound framework. Unlike the traditional three-phase Benders decomposition and column generation method, which does not guarantee optimality, we propose a branching scheme that enables the approach to determine an optimal solution to the VSPP. The approach is enhanced through practical acceleration strategies. Extensive computational results using data instances from one of the world's largest seaports show that these acceleration strategies significantly improve the performance of our solution approach and that it can obtain optimal or near-optimal solutions for instances of realistic scale. We show that our solution approach outperforms the method commonly used for solving similar problems. We perform sensitivity tests to demonstrate the robustness of the approach against variations in problem settings. We also show the benefits brought by integrated optimization by comparing our solution approach with a method that handles berth allocation and pilotage planning sequentially. [ABSTRACT FROM AUTHOR]

Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates an integrated model for the continuous berth allocation and quay crane scheduling problem by considering quay crane coverage range, ship fuel consumption, and transshipment costs. A nonlinear mixed-integer programming model is proposed. Some nonlinear parts in this model are linearized by approximation approaches. While the objective function aims to minimize waiting costs, it also seeks to minimize fuel consumption costs from the current port to the next port and housekeeping costs generated by transshipment between vessels. A local branching-based solution algorithm is designed to solve the proposed model. Computational experiments are conducted to validate the effectiveness of the proposed scientific programming model and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Berths and yards are the most critical parts of container terminals. Their efficiency directly influences the terminal operation efficiency. The multi-period coordinated optimization on berth allocation and yard assignment has been studied considering a multi-period interaction between the berth and yard. The coordinated optimization problem is formulated as an integer programming, aimed at minimizing the total truck travel distance, where the decision variables are the berthing positions for visiting vessels and the storage positions for export containers. In this work, the complexity of this problem is theoretically demonstrated. A tabu genetic algorithm is designed to solve the problem to obtain the optimal berthing and export container stacking positions. With this algorithm, the rule is applied to generate the initial feasible solutions, and crossover and mutation operations are simultaneously applied to optimize the initial solutions. Finally, two different scenarios, the determinate berth allocation scenario and the determinate yard assignment scenario, are presented to compare the influence of berth allocation and yard assignment on truck travel distance. The numerical experiments demonstrate that the berthing positions, stacking positions and the period segments directly affect the truck travel distance. And the multi-period coordinated optimization problem can effectively shorten the truck travel distance and improve the efficiency of the terminal operation, which supports the decisions of terminal operators and maintains the efficient development of the terminal.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Growing global trade brings an increasing challenge to intelligent maritime transportation, which is an important branch of the intelligent transportation system. Developing efficient technologies to improve the performance of intelligent maritime transportation is especially important. Most existing works for integrated berth allocation and quay crane assignment assume that all the equipment is available over the time horizon, however, there exists frequently time-consuming quay crane maintenance activities in the maritime port. It is recognized that maintenance activities can impact the loading/unloading activities. In this paper, we study a new bi-objective optimization model of integrated berth allocation and quay crane assignment with preventive quay crane maintenance activities. The two objectives are minimizing the total turnaround time of vessels and the total penalty cost of quay crane maintenance earliness and tardiness. For the considered problem, an appropriate integer linear programming model is formulated, and an $\varepsilon $ -constraint-based two-phase iterative heuristic is designed based on the characteristics of our problem. Computational results on a case study and randomly generated instances show the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]