Dynamic Berth Allocation Research Articles

Neural network assisted branch and bound algorithm for dynamic berth allocation problems

One of the key challenges in maritime operations at container terminals is the need to improve or optimize berth operation schedules, thus allowing terminal operators to maximize the efficiency of quay usage. Given a set of vessels and a set of berths, the goal of the dynamic berth allocation problem is to determine the allocation of each vessel to a berth and the berthing time that minimizes the total service time. This problem can be solved using exact solution methods such as branch and bound (BB) algorithms or heuristic methods, however, exact methods do not scale to large-scale terminal operations. To this end, this paper proposes a BB algorithm in which branching decisions are made with a deep neural network. The proposed exact algorithm utilizes the search order of nodes based on the output of the neural network, with the goal of speeding up the search. Three types of solution representations are compared, along with machine learning models are created for each of them. Computational results confirm the effectiveness of the proposed method, which leads to computation times that are on average around half of those without the neural network.

Dynamic berth allocation under uncertainties based on deep reinforcement learning towards resilient ports

With the evolving global trade landscape and the post-pandemic effects, the resilience of ports has become paramount. The unforeseen disturbances bring substantial challenges, especially in berth allocation, a vital task ensuring seamless resilient port operations. The unpredictability of vessel arrivals and the variability in loading/unloading times intensify these issues, pushing traditional static allocation methods beyond their limits. Fortunately, the advent of smart ports has led in an era of big data availability, enabling the application of advanced deep reinforcement learning (DRL) techniques. To capitalize on this shift, this research presents a DRL-based methodology specially designed to solve the berth allocation problem with the uncertainties in vessel arrival and container handling time to enhance port resilience. A Markov Decision Process model (MDP) of the berth allocation problem is established to minimize the mean waiting time with tailored state space, rule-based action space, and reward function to address the issue. An offline training method is designed to train the agent in selecting the optimal action based on the current state of the port berth system at each decision point even in uncertain environments, deep Q-network (DQN) is implemented for this problem. Comprehensive experiments across different problem scales are conducted to validate the effectiveness and generality of the proposed method in solving berth allocation challenges under uncertain conditions. Furthermore, the trained model also performs better than other methods in different vessel congestion levels through learning.

Enhanced Ant Colony Algorithm for Discrete Dynamic Berth Allocation in a Case Container Terminal

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).

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

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.

A Disjunctive Graph Solution Representation for the Continuous and Dynamic Berth Allocation Problem

A Disjunctive Graph Solution Representation for the Continuous and Dynamic Berth Allocation Problem

A Comprehensive Modeling of the Discrete and Dynamic Problem of Berth Allocation in Maritime Terminals

In this study, the discrete and dynamic problem of berth allocation in maritime terminals, is investigated. The suggested resolution method relies on a paradigm of optimization with two techniques: heuristic and multi-agent. Indeed, a set of techniques such as the protocol of negotiation named contract net, the multi-agent interactions, and Worst-Fit arrangement technique, are involved. The main objective of the study is to propose a solution for attributing m parallel machines to a set of activities. The contribution of the study is to provide a detailed modeling of the discrete and dynamic berth allocation problem by establishing the corresponding models using a multi-agent methodology. A set of numerical experiments are detailed to prove the performance of the introduced multi-agent strategy compared with genetic algorithm and tabu search.

Correction to: The impact of time windows constraints on metaheuristics implementation: a study for the Discrete and Dynamic Berth Allocation Problem

Correction to: The impact of time windows constraints on metaheuristics implementation: a study for the Discrete and Dynamic Berth Allocation Problem

Population-based Metaheuristics for the Dynamic Minimum Cost Hybrid Berth Allocation Problem

This study considers the Dynamic Minimum Cost Hybrid Berth Allocation Problem (DMCHBAP) with fixed handling times of vessels. The objective function to be minimized consists of three components: costs of positioning, waiting, and tardiness of completion for all vessels. A mathematical formulation of DMCHBAP, based on Mixed Integer Linear Programming (MILP), is proposed and used within the framework of commercial CPLEX 12.3 solver. As the speed of finding high-quality solutions is of crucial importance for an efficient and reliable decision support system in container terminal, two population-based metaheuristic approaches to DMCHBAP are proposed: combined Genetic Algorithm (cGA) and improvement-based Bee Colony Optimization (BCOi). Both cGA and BCOi are evaluated and compared against each other and against state-of-the-art solution methods for DMCHBAP on five sets of problem instances. The conducted computational experiments and statistical analysis indicate that population-based metaheuristic methods represent promising approaches for DMCHBAP and similar problems in maritime transportation.

The impact of time windows constraints on metaheuristics implementation: a study for the Discrete and Dynamic Berth Allocation Problem

The impact of time windows constraints on metaheuristics implementation: a study for the Discrete and Dynamic Berth Allocation Problem

A novel mathematical formulation for solving the dynamic and discrete berth allocation problem by using the Bee Colony Optimisation algorithm

Berth allocation is one of the crucial points for efficient management of ports. This problem is complex due to all possible combinations for assigning ships to available compatible berths. This paper focuses on solving the Berth Allocation Problem (BAP) by optimising port operations using an innovative model. The problem analysed in this work deals with the Discrete and Dynamic Berth Allocation Problem (DDBAP). We propose a novel mathematical formulation expressed as a Mixed Integer Linear Programming (MILP) for solving the DDBAP. Furthermore, we adapted a metaheuristic solution approach based on the Bee Colony Optimisation (BCO) for solving large-sized combinatorial BAPs. In order to assess the solution performance and efficiency of the proposed model, we introduce a new set of instances based on real data of the Livorno port (Italy), and a comparison between the BCO algorithm and CPLEX in solving the DDBAP is performed. Additionally, the application of the proposed model to a real berth scheduling (Livorno port data) and a comparison with the Ant Colony Optimisation (ACO) metaheuristic are carried out. Results highlight the feasibility of the proposed model and the effectiveness of BCO when compared to both CPLEX and ACO, achieving computation times that ensure a real-time application of the method.

New hybrid genetic algorithms to solve dynamic berth allocation problem

Berth allocation problem (BAP) is concerned to assign ships to port terminal positions, seeking to minimize the total service time and maximize the quay occupation. A dynamic model with special features is developed to deal with real scenarios from Port Administration of Paranaguá and Antonina (APPA), located on the Brazilian coast. To solve the problem, this work proposes two metaheuristics comprise by a novel combination of genetic algorithm and an approximated dynamic programming employed as a local search. Two heuristics for solution space reductions, a confinement procedure in a reduced neighborhood, known as Corridor Method, and an elimination process for unpromising solution regions are designed for local search approach. Case studies present a problem complexity discussion and comparative analysis of the metaheuristics regarding two standard genetic algorithms. The computational experiments explore the optimal solutions for small instances and the best results, solutions variability, and probabilistic plots resorting by ten instances based on real data available by APPA. The results show the reliability of the metaheuristics to deal with large instances and tight schedules in busy port systems.

Simulation optimization of the berth allocation in a container terminal with flexible vessel priority management

ABSTRACT Solving the berth allocation problem (BAP) in ports is not trivial where the berth resources are limited and various sizes of vessels arrive with dramatically dissimilar loads. Especially in real scenarios, arriving vessels are accepted for a berth with the first come first served (FCFS) priority rule. This study proposes a decision support system coupled with a simulation optimization module based on the swarm-based Artificial Bee Colony optimization algorithm for solving the BAP. The proposed methodology was implemented for the Izmir port in Turkey. To investigate the influences of the vessel priorities on the BAP, four different experimental scenarios based on the single (SQM) and multiple queue models (MQM) were coupled with FCFS and proposed hybrid queue priority (HQP) rule. The results indicated that SQM scenarios were superior to MQM scenarios in a manner of minimizing the average vessel waiting times and the implementation of a dynamic berth allocation strategy for the MQM significantly decreases the vessel waiting times. Results of the SQM also imply that utilization of the HQP approach further minimizes the average vessel waiting times and increases the berth utilization and port throughput without yielding excessive waiting times for the larger vessels compared with the FCFS priority rule.

Comparison of Planning Models for Dynamic Berth Allocation Problem Using A Sailfish-based Algorithm

Abstract Decide when and where allocate incoming ships at a container terminal is among the main problem confronted by port operators. This problem often called the Berth Allocation Problem (BAP).In this work, we realize an analytical study on the major planning models presented in the literature for the berth allocation problem, namely dynamic and discrete berth allocation problem model (DDBAP), dynamic and continuous berth allocation problem model (DCBAP) and dynamic and hybrid berth allocation problem model (DHBAP). The objective of this study is determine which model among the mentioned above can be used as a standard model for all type of terminal layouts and which of them ensure satisfactory outcome at level of minimization of ships turnaround time in the port. A commercial solver (CPLEX) as well as an efficient sailfish-based algorithm (SFA) are used to solve each model. Finally, computational experiences and comparison have been presented to demonstrate the most suited planning model.

Berth allocation problem: formulation and a Tunisian case study.

This paper examines one of the most important operational problems in seaport terminals, namely the Berth Allocation Problem (BAP) which finds an optimal assignment of ships to the berths that minimize the total waiting time of all ships and reduce congestion in ports. Our problem is to affect and schedule n ships on m berths to minimize the processing time and the waiting time for all the ships in the port. Therefore, ships stay time in the port known by the flow time, while respecting the physical constraints existing at the port (such as the depth of the water berth and the draft of the ship’s water), knowing that each ship can only accommodate one ship at a time. It is as if it was a case of n tasks and m machines in parallel, and we wanted to schedule the passage of different tasks on different machines, knowing that each task can only pass on one machine and that the interruption of the task is not allowed. For example, if a job started on a machine, it will remain on this machine up to its completion. In our case, tasks are ships and machines are berths that are opting to minimize the total flow time and, therefore, to decrease the residence time of all the ships in the port. In a first step, a Mixed Integer Linear Program model is designed to address the BAP with the aim of minimizing the flow time of the ships in the port, our sample can be used for both static and dynamic berth allocation cases. In a second step, this model is illustrated with a real case study in the Tunisian port of Rades and solved by a commercial solver CPLEX. Calculation results are presented and compared with those obtained by port authorities in Radès.

Novel formulations and modeling enhancements for the dynamic berth allocation problem

Abstract This paper addresses the well-known dynamic berth allocation problem (DBAP), which finds numerous applications at container terminals aiming to allocate and schedule incoming container vessels into berthing positions along the quay. Due to its impact on ports’ performance, having efficient DBAP formulations is of great importance, especially for determining optimal schedules in quick time as well as aiding managers and developers in the assessment of solution strategies and approximate approaches. In this work, we propose two novel formulations, a time-indexed formulation and an arc-flow one, to efficiently tackle the DBAP. Additionally, to improve computational performance, we propose problem-based modeling enhancements and a variable-fixing procedure that allows to discard some variables by considering their reduced costs. By means of these contributions, we improve the models’ performance for those instances where the optimal solutions were already known, and we solve to optimality for the first time other instances from the literature.

The use of frontier techniques to identify efficient solutions for the Berth Allocation Problem solved with a hybrid evolutionary algorithm

The search for logistics best-practices in international trade has led to the appearance of the Berth Allocation Problem. If the vessels have release dates, the problem is proved to be NP-hard and the performance of exact algorithms is not satisfactory, leading to the use of metaheuristics. This paper develops a Hybrid Evolutionary Algorithm for the discrete and dynamic Berth Allocation Problem. A challenge of using Genetic Algorithms is the identification of the best approach to model a specific problem. This paper proposes the use of frontier techniques (Data Envelopment Analysis and Free Disposal Hull models) to compare the performances of alternative specifications of the parameters for the algorithm proposed and to identify efficient solutions.

A Dynamic and Flexible Berth Allocation Model with Stochastic Vessel Arrival Times

This study proposes a berth-flow network modeling approach to deal with the dynamic berth allocation problem (DBAP) with stochastic vessel arrival times. In this approach, uncertain vessel arrival times are represented using discrete probability distributions and a flexible berth allocation scheme based on the blocking plan concept is incorporated into the model to effectively utilize wharf space. This new model is referred to as the stochastic dynamic (vessel arrival) and flexible (berth space) berth allocation problem (SDFBAP) model. The aim is to minimize the sum of the expected values of unanticipated schedule delay costs and the penalties for being unable to service all vessels within the planning horizon. The proposed model is formulated as an integer multi-commodity network flow problem which can be solved with off-the-shelf solvers. Computational experiments are conducted using a real example to demonstrate the effectiveness and efficiency of the SDFBAP model. A simulation-based approach is adopted to evaluate the SDFBAP model. A number of scenario analyses are also conducted to gain insight into important model parameters.

A modified sailfish optimizer to solve dynamic berth allocation problem in conventional container terminal

A modified sailfish optimizer to solve dynamic berth allocation problem in conventional container terminal

A hybrid dynamic berth allocation planning problem with fuel costs considerations for container terminal port using chemical reaction optimization approach

This paper investigates the dynamic ship berth allocation problem for a container handling port, focusing on vessel waiting time at the anchorage due to the unavailability of the berth and quay cranes. A mixed integer linear programming model considering the fuel cost associated with waiting time and operational time of the docked vessel is developed. The hiring of the quay cranes to load/unload the containers from the ship and arrangement of the vessels in different berths is taken into account. Fuel consumed by the vessels while performing their respective port operations is incorporated in the model for addressing the sustainability aspects in berth allocation problem. A chemical reaction optimization algorithm is proposed to solve the problem in a large-scale realistic environment and compared with the results with block-based genetic algorithm, genetic algorithm and particle swarm optimization. The computational experiment illustrates and validates the proposed model on a real case scenario of the port located in India. The case shows that the developed model achieves better utilization of port resources and available berths.