Berth Allocation Research Articles

A new multi-objective model for berth allocation and quay crane assignment problem with speed optimization and air emission considerations (A case study of Rajaee Port in Iran)

A new multi-objective model for berth allocation and quay crane assignment problem with speed optimization and air emission considerations (A case study of Rajaee Port in Iran)

An Enhanced NSGA-II for Solving Berth Allocation and Quay Crane Assignment Problem With Stochastic Arrival Times

The berth allocation and quay crane assignment problem (BACAP) is an important port operation planning problem. To obtain an effective and reliable schedule of berth and quay crane (QC), this study addresses the BACAP with stochastic arrival times of vessels. An efficient method combining scenario generation is presented to simulate the stochastic arrival times. After then, a mixed integer linear programming (MILP) model is established, aiming to minimize the expectation of the vessels’ total stay time in port. A multi-objective constraint-handling (MOCH) strategy is adopted to reformulate the developed model, which converts constraint violations into an objective, thus transforming the single-objective optimization model with complex constraints into a dual-objective optimization model with only easy-handling constraints. Then an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is proposed to solve the dual-objective model, in which a neighborhood search algorithm and a search bias mechanism are incorporated to strengthen the local exploitation capability. Furthermore, a repair method (RM), penalty function (PF) and the superiority of feasible solutions (SF) strategy for constraint handling are designed respectively and incorporated with genetic algorithm to solve the original single-objective optimization model. Finally, numerical experiments on instances in the literature are conducted to validate the effectiveness of the MOCH and the proposed ENSGA-II. The results show that the average total stay time of vessels is reduced when stochastic arrival times are considered. Comparison results with another two multi-objective methods and three single-objective methods combined with different constraint-handling strategies corroborate the superiority of the proposed ENSGA-II and MOCH.

Dynamic berth allocation problem for a novel hybrid berth system in container terminal

Dynamic berth allocation problem for a novel hybrid berth system in container terminal

Genetic based algorithms to solving multi-quays berth allocation problem with setup time constraints

Genetic based algorithms to solving multi-quays berth allocation problem with setup time constraints

A column generation-based heuristic to solve the integrated planning, scheduling, yard allocation and berth allocation problem in bulk ports

A column generation-based heuristic to solve the integrated planning, scheduling, yard allocation and berth allocation problem in bulk ports

Berth Allocation and Crane Assignment Problem in Continuous Berth Containers Vessels Terminals

Berth Allocation and Crane Assignment Problem in Continuous Berth Containers Vessels Terminals

Overall Scheduling Model for Vessels Scheduling and Berth Allocation for Ports with Restricted Channels That Considers Carbon Emissions

As maritime transportation develops, the pressure of port traffic increases. To improve the management of ports and the efficiency of their operations, vessel scheduling must be optimized. The vessel scheduling problem can be divided into channel scheduling and berth allocation. We considered the complex problem of vessel scheduling in a restricted channel and the berth allocation problem, and a combined model that considers carbon emissions was developed. This model should reduce vessel waiting times, improve the quality of the berth loading and unloading service, meet the requirements of “green” shipping, and improve the overall scheduling efficiency and safety of ports. An adaptive, double-population, multi-objective genetic algorithm NSGA-II-DP is proposed to calculate the mathematical model. In the case study, the rationality verification and sensitivity analysis of the model and algorithm are conducted, and the NSGA-II-DP and NSGA-II were compared. Results demonstrate that the overall convergence of the NSGA-II-DP algorithm is better than that of NSGA-II, demonstrating that the NSGA-II-DP algorithm is a useful development of NSGA-II. In terms of port scheduling, the results of our model and algorithm, compared with the decisions provided by the traditional First Come First Service (FCFS) strategy, are more in line with the requirements for efficiency and cost in the actual port management, and more dominant in the port management can provide better decision support for the decision-makers.

Berth planning and real-time disruption recovery: a simulation study for a tidal port

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.

An Adaptive Ant Colony System Based on Variable Range Receding Horizon Control for Berth Allocation Problem

The berth allocation problem (BAP) is an NP-hard problem in maritime traffic scheduling that significantly influences the operational efficiency of the container terminal. This paper formulates the BAP as a permutation-based combinatorial optimization problem and proposes an improved ant colony system (ACS) algorithm to solve it. The proposed ACS has three main contributions. First, an adaptive heuristic information (AHI) mechanism is proposed to help ACS handle the discrete and real-time difficulties of BAP. Second, to relieve the computational burden, a divide-and-conquer strategy based on variable-range receding horizon control (vRHC) is designed to divide the complete BAP into a set of sub-BAPs. Third, a partial solution memory (PSM) mechanism is proposed to accelerate the ACS convergence process in each receding horizon (i.e., each sub-BAP). The proposed algorithm is termed as adaptive ACS (AACS) with vRHC strategy and PSM mechanism. The performance of the AACS is comprehensively tested on a set of test cases with different scales. Experimental results show that the effectiveness and robustness of AACS are generally better than the compared state-of-the-art algorithms, including the well-performing adaptive evolutionary algorithm and ant colony optimization algorithm. Moreover, comprehensive investigations are conducted to evaluate the influences of the AHI mechanism, the vRHC strategy, and the PSM mechanism on the performance of the AACS algorithm.

A two-stage stochastic programming model for seaport berth and channel planning with uncertainties in ship arrival and handling times

The limited availability of berths and channels is generally the bottleneck restricting the capacity of a seaport and thus resulting in traffic congestion. Optimizing the operations of the berths and channels has been recognized as a more economic avenue for mitigating seaport traffic congestion compared with channel dredging and berth expanding that needs significant capital and time costs. This paper presents a two-stage stochastic mixed integer linear programming model for the seaport berth and channel planning, aiming to minimize the expected total weighted completion times of ships under uncertain ship arrival times and ship handling durations. The first stage decides the berth allocation of ships under uncertainty. In the second stage, the channel planning, including the selection of lanes, assignment of tugboats, and sequencing of ships, is determined after the uncertainty has been realized. To effectively solve the model, we propose two tailored decomposition methods, that is, the stage decomposition method and the decomposition-based heuristic algorithm (DHA). Then, a lower bound of the problem is derived to evaluate the quality of the solution. Numerical experiments on Tianjin Port of China show the satisfactory performance of these two proposed methods. Especially, the DHA is able to obtain near-optimal solutions with the average optimality gap less than 3% within four-hour computational time for the instances up to 500 scenarios and 190 ship movements. Some managerial insights are obtained to guide the operations of the port.

A simulation study on the configuration of quay cranes in container terminal

ABSTRACT The configuration of the equipment is essential when designing a container terminal. The quay crane (QC) is one of the most important equipment in the container terminal because it decides the capacity of the quayside operation and berth service. A macroscopic simulation model of the quayside operation and berth service in the container terminal is proposed in this study. An integrated berth allocation and QC scheduling strategy is utilised in the simulation. The long-term simulation experiments considering the annual seasonal variation are conducted. And an appropriate configuration of the QCs is selected according to the analysis for key indicators of berth service and quayside operation. The comprehensive analysis indicates the selected configuration is great when the throughput is 2.5 million TEUs and it also can meet the quayside operation when the throughput increases by 20%.

Data-driven ship berthing forecasting for cold ironing in maritime transportation

Cold ironing (CI) is an electrification alternative in the maritime sector used to reduce shipborne emissions by switching from fuel to electricity when a ship docks at a port. During the ship’s berthing mode of operation, accurately estimating the berthing duration could assist the port operator to manage the berth allocation and energy scheduling optimally. However, the involvement of multiple input parameters with a large dataset requires a suitable handling method. Thus, this paper proposed a data-driven approach for ship berthing forecasting of cold ironing with various models such as artificial neural networks, multiple linear regression, random forest, decision tree, and extreme gradient boosting. Meanwhile, RMSE and MAE are two main indicators applied to assess forecasting accuracy. The simulation-based result shows that the artificial neural network outperforms all other models with the lowest error performance of RMSE (3.1343) and MAE (0.2548), suggesting its capability to handle nonlinearities in complex forecasting problems of port activity. The high accuracy of forecasting output in this study, which is berthing duration contributes to close estimation of two info: 1) CI power consumption and 2) departure time of the ship. This information is vital to the port operator to be used in the energy management system (EMS) as well as in the berth allocation problem (BAP).

Hybrid rolling-horizon optimization for berth allocation and quay crane assignment with unscheduled vessels

Port operations usually suffer from uncertainties, such as vessels’ arrival time and handling time and unscheduled vessels. To address this, this study presents a dynamic berth allocation and crane assignment specific problem (BACASP) when unscheduled vessels arrive at the port, which is branded the berth allocation and quay crane assignment specific problem with unscheduled vessels (UBACASP). A rolling-horizon based method is proposed to decompose the UBACASP into a multi-stage static decision BACASP, wherein a rescheduling margin-based hybrid rolling-horizon optimization method is developed by incorporating the event-driven and periodical rolling-horizon strategies as the urgency of dynamic events is evaluated. In each rolling horizon, a mixed integer linear programming model (MILP) is presented for the BACASP to minimize the total port stay time of vessels and the penalties of delays associated with the spatial and temporal constraints, such as the length of continuous berth, number of quay cranes (QCs) and non-crossing of QCs. A discretization strategy is designed to divide the continuous berth into discrete segments, and convert the BACASP to a discrete combinatorial optimization problem, which is efficiently solved by the proposed adaptive large neighborhood search algorithm (ALNS). Case studies with different problem characteristics are conducted to prove the effectiveness of the solution methods proposed in this study. Moreover, the performances of the ALNS and the existing methods for solving the BACASP are compared, and the advantages and disadvantages of different rolling strategies under different degrees of uncertainties are deeply analyzed.

Static and Discrete Berth Allocation for Large-Scale Marine-Loading Problem by Using Iterative Variable Grouping Genetic Algorithm

In this paper, we study the static discrete berth allocation problems (BAPs) for large-scale time-critical marine-loading scenarios. The objective is to allocate the vessels to different types of berths so that all the vessels can be loaded within the minimum time under the tidal condition. The BAP is formalized as a min–max problem. This problem is rather complex as the vessels and berths are quite numerous in the large-scale marine-loading problem. We analyze this problem from a novel perspective, and find out that this problem has the characteristic of partially separable. Therefore, the iterative variable grouping genetic algorithm (IVGGA) is designed to search the near-optimal berth allocation plans. The vessels and berths are divided into subgroups, and the genetic algorithm (GA) is applied to generate the near-optimal berth allocation plans in each subgroup. To achieve the balance of loading tasks among subgroups, we propose reallocating some vessels among subgroups according to the berth allocation plans in subgroups. To guarantee the convergency of the algorithm, an iterative vessel reallocation policy is devised considering the loading tasks of different types of berths. We demonstrate the proposed algorithm in dealing with large-scale BAPs through numerical experiments. According to the results, we find that the proposed algorithm would have good performance when the number of vessels in each subgroup are kept in medium scale. Compared with the original GA, our algorithm shows the effectiveness of the iterative variable grouping strategy. The performance of our algorithm is almost not changed as the number of vessels and berths increases. The proposed algorithm could obtain efficient berth allocation plans for the large-scale marine-loading problem.

Effect of Service Priority on the Integrated Continuous Berth Allocation and Quay Crane Assignment Problem after Port Congestion

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.

Robust berth scheduling using machine learning for vessel arrival time prediction

In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach.

Investigation of berth allocation problem in container ports considering the variety of disruption

While a variety of disruptions are involved in the daily operation of container ports, the disruption management process in practice highly relies on the port operators’ experience or follows simple service rules. A need exists for a systematic way to adopt mathematical models to deal with these uncertainties. Considering the significance of the berth in the ports, we investigate the berth allocation problem (BAP) involved with different disruptions from a proactive perspective. We propose a mathematical model and an algorithm that can help operators make robust berth allocation schedules in a more maneuverable way instead of relying on their working experience. The research is presented in the following steps: initially, potential port disruptions related to the BAP are identified; then, based on the identification process, a proactive optimization model is formulated to generate baseline berth allocation schedule, minimizing the baseline schedule cost in the deterministic situation and the recovery cost in the disruption scenarios; finally, due to the characteristic of the BAP, a multi-stage heuristic algorithm is developed to solve large-scale problems. Algorithmic solutions to randomly generated computational instances are compared with the CPLEX solver to validate the reliability and the effectiveness of the algorithm. The difference in the recovery cost and the total cost between the proactive model and a deterministic model shows that the proposed model can generate berth allocation schedules of better robustness and maneuverability that help in practice.

Distributionally robust optimization for the berth allocation problem under uncertainty

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.

Berth Allocation at Passenger Terminals Using Auctions

In this paper, we compare a multi-round, second-price, sealed-bid bundle auction and a single-item, sequential, second-price, sealed-bid auction for berth slot leasing for vessels (roll-on/roll-off passenger vessels and/or cruise ships) at a public marine terminal. The bundle auction mechanism is designed to maximize port operator profits by auctioning berth (time) slots in groups. The framework is tested using simulation by varying: the number of roll-on/roll-off passenger/cruise ship operating companies; the number of slots they bid for; and the mechanism design with regards to the winner determination, slot valuation, and max to min slot bid ratio among the bidders. The results indicate that neither auction type is a clear winner, and depending on the assumptions, a terminal operator should choose one over the other. The results from this study can be used by terminal operators, given their knowledge and/or assumptions on slot valuations and demand, to select a winner-determination policy and the minimum number of slots they allow players to bid for when designing the auction of their berth capacity to maximize their profits.

Distributed Optimal Voltage Control and Berth Allocation of All-Electric Ships in Seaport Microgrids

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.