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The ever-increasing worldwide container throughput and the intensified port competition have demanded better terminal operational performance, which is usually measured by the gross crane rate (GCR). In this paper, a multiagent-based simulation model is proposed to investigate how the block length of the storage yard and the tactical-level stacking policy affect the operational performance of non-transshipment container terminals over the long term. Experiments consider seven block lengths, two yard truck fleet sizes and two stacking policies. The results demonstrate that the best block length yielding the highest GCR is dependent on the stacking policy and the yard truck fleet size, and the separate stacking policy is essentially superior to the scattered stacking policy. Specifically, only when 9-yard trucks are deployed per quay crane under the separate stacking policy, can the typical 42-slot block length achieve the highest GCR. Although the experiments are not exhaustive, they do provide the first fundamental insights, with respect to the effects of block lengths and stacking policies, on the long-term performance at a manually controlled non-transshipment container terminal. [ABSTRACT FROM AUTHOR]

Terminal layout design is an important strategic decision to obtain efficient automated container terminals. Recently, several types of layout design are being implemented to realize side-loading operations, such as U-type and parallel layout design for bilateral-cantilever automated rail-mounted gantry cranes. However, it is lack of a comprehensive comparation with perpendicular layout design for non-cantilever gantry cranes, which allows end-loading operations. Besides, the impact of layout design on terminal performance, such as annual throughput, ship waiting time and operation efficiency of quay cranes, is still not well understood and needs to be estimated in the planning phases of automated terminals. Thus, this study conducts a detailed simulation research on different types of layout design to compare their terminal performance. Novel agent-based simulation models are developed, which incorporate side-loading and end-loading operations under different types of layout design. And simulation-based energy consumption and cost models are developed to evaluate their energy consumption and total cost. Results indicate that U-type automated terminals gain the lowest energy consumption and operation cost, and outperform those with perpendicular layout in terms of operation efficiency and waiting time in most cases. The valuable reference regarding layout design is identified for terminal operators to make strategic decisions. • Novel simulation models are developed to comprehensively evaluate layout design in automated terminals. • Models consider special operation characteristics, cycle operation processes and many practical settings. • Influence factors are identified to improve operation efficiency and reduce waiting time. • U-type automated terminals gain the lowest energy consumption and operation cost. [ABSTRACT FROM AUTHOR]

Effective prediction of ship arrivals should provide the estimated delay or advance of arrival ships with greater accuracy, and improve the performance of container terminal operations. Therefore, taking Gangji (Yining) Container Terminal (GYCT), China, as an example, this paper resorts to data mining approaches to predict ship arrivals and explore the value of such predicted ship arrivals on the container terminal operation. First, this study applies three data mining approaches, including Back-Propagation network (BP), Classification and Regression Tree (CART) and Random Forest (RF), to estimate the delay or advance of ship arrivals using the collected data of ship arrivals. Then the predictive performance of these three approaches is compared and discussed, it is concluded that RF performs better than BP and CART, and ETA month and ship length are the most important determinants of ship arrivals in GYCT terminal. Finally, series simulation experiments are conducted to assess the value of the ship arrivals predicted by RF model on the improvement on daily operation planning of berth allocation and quay crane assignment in the GYCT terminal. And the results show that incorporating the predicted ship arrivals by RF model is beneficial to improve the performance of operation planning of GYCT terminal. [ABSTRACT FROM AUTHOR]

The conventional approach of designing a container yard should be reexamined in the context of sustainable port development. Considering the uncertain future throughput, a simulation-based optimization framework is proposed to obtain a cost-effective and reliable design solution to the physical layout and equipment deployment strategy of the yard at a mega container terminal. In this framework, a two-stage stochastic programming model is presented aided with a simulation procedure of terminal operations. Finally, an application is given and the results show that the proposed integrated decision framework is effective and helpful for optimizing container yard design in the context of sustainable development of container terminals. [ABSTRACT FROM AUTHOR]

Electric rubber tire container gantry cranes (ERTGs) have already been widely used to replace rubber tire container gantry cranes (RTGs) in many ports of the world, especially in China. ERTGs can reduce carbon dioxide emissions by shifting energy demand from diesel to electricity (energy replacement). It takes almost 6–12 months to change a RTG to an ERTG, and the changed yard cranes cannot work during the replacement phase due to the mechanical reformation and electrical power system construction etc. Therefore, the yard crane network will generate bottlenecks in the container flow and influence the service level when energy replacement happens. This paper focuses on the problem of allocating limited resources for yard cranes to reduce the carbon emissions. The main challenges are how to solve the energy replacement problem at a network level and how to cope with the high uncertainties in the container terminal transportation network. Therefore, we model the energy replacement problem with the purpose of minimizing the carbon emissions by combining an allocation resource mathematical model and a simulation model of the whole transportation network together. The first stage of the energy replacement problem is to make energy replacement decisions each year, while the second stage is to evaluate the value of a seaport's service level based on the realized decisions by running the simulation model. However, the service level indicator is a random variable dependent on the decisions in the mathematical model, which should be controlled to make the seaport work normally at a network level. As an example, the optimization and simulation procedures are applied to a major container terminal in China. The proposed model is general and can be applied to the energy replacement problem of any other seaport. [ABSTRACT FROM AUTHOR]

This paper explores the effect of entrance channel dimensions on berth occupancy of a container terminal when making master planning for a new container terminal, to avoid possible bottlenecks for port's future performance. Therefore, a process-interaction-based simulation model is first developed to simulate container terminal operation, especially ship operation in entrance channel. Then a series of simulation experiments are conducted, in which dozens of container terminals with different numbers of berths and channel dimensions are considered to reproduce the microscopic, stochastic, real-time environment. Finally, simulation results show that the berth occupancy depends on entrance channel dimensions, and more berths, two-way traffic channel and less travel time in channel have higher berth occupancy given the same port service level. So that the effect of entrance channel dimensions, especially very long one-way traffic channels, on berth occupancy should be quantified when determining berth capacities at the master planning stage. Moreover, the analysis and simulation model presented in this paper may help port authorities and the planners to achieve a harmonized overall design in practice. [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]

Green shipping and efficient information sharing deepen the collaboration between shipping companies and ports, enabling vessel speed optimization (VSO) to be integrated with the berth allocation and quay crane assignment problem (BACAP). However, the literature presented to date usually considers all vessels as a whole and only addresses the vertical conflict between the two levels of the BACAP and the VSO but neglects the horizontal conflict among vessels in each level of the collaborative planning. To describe and address the conflicts within and between levels, we consider the vessel service differentiation (VSD) and propose a BACAP-VSO with VSD (BACAP-VSO-w/-VSD) collaboration mode. To this end, we set up a bi-level multi-objective optimization model. In the upper-level BACAP model, the VSD is implemented by separating vessels into different preferential groups. Multi-objectives are formulated to minimize the service delays of vessels in preferential groups and all vessels. In the lower-level VSO model, the VSD is realized by grouping vessels based on their shipping companies. Multi-objectives are solved to minimize the fuel consumption costs of each shipping company. We develop a nested genetic algorithm incorporated with an inheritance factor and jumping operations for the solution approach. Experimental results show that the proposed collaboration mode can analyze trade-offs among multiple conflicting objectives in any and between both planning levels. Compared to the existing collaboration mode, the proposed collaboration mode further reduces service delays of vessels and fuel consumption costs of shipping companies, improves customer satisfaction, and effectively reduces vessel emissions. • Proposes a new collaboration mode for port operation and vessel speed reduction. • Differentiates vessel service to depict both vertical and horizontal conflicts among vessels. • Establishes a bi-level multi-objective optimization model to achieve the collaboration mode. • Develops a nested genetic algorithm to address well-defined problem instances. • Finds optimal solutions for ports and shipping companies and outline managerial implications. [ABSTRACT FROM AUTHOR]

• An integrated vehicle charging and operation scheduling problem in ACTs is investigated. • A decomposition-iteration algorithm is proposed to solve this problem. • A simulation-based optimization method is designed to accelerate the solving process. The increasing utilization of battery-powered automated guided vehicles in automated container terminals, has an important consequence on terminal cost and efficiency. How to tackle integrated vehicle charging and operation scheduling problem to maintain high terminal performance is prominent for sustainable port operation. In this paper, fast charging technology is investigated, and a mixed integer programming model for this complicated scheduling problem is constructed, which aims to reduce charging cost and penalty cost related to makespan, and includes sequence-related constraints, time-related constraints and energy-related constraints. A decomposition-iteration algorithm is proposed to solve this problem, and furthermore it is combined with a simulation-based optimization method to address practical-sized instances. Numerical experiments on real-world cases are conducted to verify the efficiency and effectiveness of the proposed solution algorithm. Insightful managerial implications are derived by comparative analysis on charging rules and charging facility locations, and sensitivity analysis on charging power, charging facility configuration and vehicle configuration. Experimental results provide valuable references for terminal managers to make configuration and scheduling decisions for battery-powered vehicle transporting systems. [ABSTRACT FROM AUTHOR]

Due to more and more awareness of environmental conservancy and sustainable development, automated container terminals (ACTs) have gained increasing attention and become popular worldwide. However, there is a crucial challenge in implementing ACTs, i.e., how to develop terminal layout planning (including terminal layout and facility selection) to realize performance goals. Focusing on this challenge, the aim of this paper is to resolve how to appropriately select terminal layout and facilities in ACTs with respect to various performance criteria. Several research limitations can be gathered from the existing literature: (i) Most of the existing works mainly develop optimization technologies to tackle layout optimization and facility comparison issues within a few types of terminal layout and facilities, yet few researchers have proposed evaluation methods for making selection decisions on terminal planning. (ii) Only a few types of criteria are considered, and requirements and preferences of different participants are ignored, not to say realizing an optimal compromise by employing comprehensive criteria weights in evaluation processes. (iii) New technological improvement in ACTs has not received much attention, and there still lacks a method to precisely quantify the concerned criteria. Accordingly, this paper introduces a criteria system comprising economic, environment, social, resource and performance groups and develops an agent-based simulation method considering different terminal layout and facilities to determine criteria values. A multi-criteria decision making (MCDM) framework is proposed for making the most reasonable and appropriate selection decisions, which employs a variation coefficient weighted method to calculate comprehensive criteria weights, which integrate subjective and objective weights obtained from AHP (analytic hierarchy process) method and entropy weight method. To overcome rank reversal problems in the TOPSIS (technique for order preference by similarity to an ideal solution) method, a new improved rank reversal robust TOPSIS method, called NR-TOPSIS, is utilized to rank and select alternatives, which is ranking stable in the experiments performed. An illustrative application on a container terminal in China demonstrates the effectiveness of the proposed MCDM method and sensitivity and comparative analysis verifies the robustness and stability of the method. [ABSTRACT FROM AUTHOR]

The contribution of this paper is to provide a method for optimizing installation capacity and operation strategy of a hybrid renewable energy system (HRES) with offshore wind energy for container terminals. A mixed integer optimization model of the HRES is proposed, in which maximum environmental and economic benefits are considered as two objectives, and energy supply and demand balances, energy storage balances, technical constraints and quantity limitations are considered as constraints. However, container terminals typically execute many complicated and stochastic operation processes, and thus represent highly dynamic energy demands. Therefore, a simulation-based optimization algorithm is developed to resolve the optimization model, which consists of a simulation model and an optimization module. The simulation model is constructed to overcome the stochastic characteristics and obtain the hourly energy demands in container terminals, which are fed into the optimization module. While, the optimization module is used to search the solution space and provide the final decisions under three evaluation strategies: cost-saving, low-carbon, and trade-off strategy. Finally, taking a container terminal in Northeast China as an example, optimal results are obtained by the proposed algorithm, which can be used to provide references for power department policy making and green container terminal construction. • Optimizing installation capacity and operation strategy of a HRES for container terminals. • Developing a simulation-based model to output the hourly energy demands in container terminals at each time step. • Proposing a simulation-based optimization method to resolve the optimization model. [ABSTRACT FROM AUTHOR]