Berth Allocation Research Articles

Mixed Integer Robust Programming Modeling for Continuous Berth Allocation with Uncertain Vessel Arrival Time

Mixed Integer Robust Programming Modeling for Continuous Berth Allocation with Uncertain Vessel Arrival Time

Applications of Stochastic Mixed-Integer Second-Order Cone Optimization

Second-order cone programming problems are a tractable subclass of convex optimization problems that can be solved using polynomial algorithms. In the last decade, stochastic second-order cone programming problems have been studied, and efficient algorithms for solving them have been developed. The mixed-integer version of these problems is a new class of interest to the optimization community and practitioners, in which certain variables are required to be integers. In this paper, we describe five applications that lead to stochastic mixed-integer second-order cone programming problems. Additionally, we present solution algorithms for solving stochastic mixed-integer second-order cone programming using cuts and relaxations by combining existing algorithms for stochastic second-order cone programming with extensions of mixed-integer second-order cone programming. The applications, which are the focus of this paper, include facility location, portfolio optimization, uncapacitated inventory, battery swapping stations, and berth allocation planning. Considering the fact that mixed-integer programs are usually known to be NP-hard, bringing applications to the surface can detect tractable special cases and inspire for further algorithmic improvements in the future.

Energy-saving-oriented Berth Scheduling Model at Bulk Terminal

With the global warming to the survival and development of mankind, more and more attention is paid to low-carbon, green and energy-saving production. As one of the main modes of international transportation, the wharf has been facing a serious problem of its high carbon-emission. In order to balance the relationship between port energy consumption and efficiency, it is necessary to study the berth allocation, loading and unloading of bulk terminal from the perspective of energy saving with the proposal of energy saving and emission reduction in China. Both energy saving and efficiency can be achieved at the bulk terminal in the study. First, the latest layout and basic operation process of the bulk terminal are obtained by analyzing its process system. Secondly, a new model of energy consumption is constructed through regression analysis, based on which a mathematical model of berth scheduling is then constructed toward the aim with the minimum cost of distance deviation, of delayed departure and of energy consumption. Finally, by taking Tianjin Port for example, the model is verified to be feasible and efficient in reducing the loading and unloading cost, the total cost of ship in port time, in improving its service quality and achieving a low-carbon production. Moreover, the model can serve as a reference for other ports to reduce mechanical energy consumption.

Using Artificial Neural Network Model for Berth Congestion Risk Prediction

The seaport plays a critical role in the global supply chain management. Today, with the increasing growth of the global containerized trade, many challenges appears for the seaport, one of the most significant challenges is the berth allocation problem BAP, this fact impact all the seaport operations and benefits. The aim of this paper is to treat one the lead causes of BAP which is the berth congestion risk. Based on an ANN algorithm we tried to predict the congestion risk which is related directly with of the delay of vessels in the berth of the seaport. In this sense, we used a real combined dataset containing many features and we treat the correlation of each feature with the dependent variable, before applying six known models in order to compare the performance of the proposed ANN model. The result ensures that the proposed model surpasses the six traditional model on the tree chosen metrics of evaluation, F1 Score F1S = 86%, Recall RS = 88% and Accuracy = 96.5% which allow us to adopt this model for our case. In addition the developers can push the model even further, by using for example the natural inspired algorithms to enhance the accuracy.

A Collaborative Berth Planning Approach for Disruption Recovery

Traditionally, terminal operators create an initial berthing plan before the arrival of incoming vessels. This plan involves decisions on when and where to load or discharge containers for the calling vessels. However, disruptive unforeseen events (i.e., arrival delays, equipment breakdowns, tides, or extreme weather) interfere with the implementation of this initial plan. For terminals, berths and quay cranes are both crucial resources, and their capacity limits the efficiency of port operations. Thus, one way to minimize the adverse effects caused by disruption is to ally different terminals to share berthing resources. In some challenging situations, terminal operators also need to consider the extensive transshipment connections between feeder and mother vessels. Therefore, in this work, we investigate a collaborative variant of the berth allocation recovery problem which focuses on the collaboration among terminals and transshipment connections between vessels. We propose a mixed-integer programming model to (re)-optimize the initial berth and quay crane allocation plan and develop a Squeaky Wheel Optimization metaheuristic to find near-optimal solutions for large-scale instances. The results from the performed computational experiments, considering multiple scenarios with disruptive events, show consistent improvements of up to 40% for the suggested collaborative strategy (in terms of costs for the terminal operators).

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 MATHEMATICAL MODEL AND SOLUTION METHOD FOR THE BERTH ALLOCATION PROBLEM WITH VARIABLE HANDLING TIME AND CONTINUOUS TIME HORIZON

In this paper, we present the integration of two problems related to the operations in a port terminal: the Berth Allocation Problem (BAP) integrated with the Machine Assignment Problem. We present a mixed-integer linear programming (MILP) formulation, capable of assigning and scheduling incoming vessels to berthing positions and the assignment of machines for handling the vessels. The machines can be quay cranes, mobile cranes, straddle carriers, forklifts, trucks, and any other machine. The problem aims to minimize the waiting time plus the handling time of the vessels. To solve the problem, we developed a heuristic algorithm, capable of solving a problem instance in seconds. To compare the results, we generate several instance problems based on real data and solve them with our MILP formulation implemented in a solver, our heuristic, and a First In First Out (FIFO) algorithm. The solver was able to find solutions only in small-scale instances, and the heuristic was able to find good solutions for all instances.

Optimal scheduling for seaport integrated energy system considering flexible berth allocation

As a bridge to connect the land and sea, the emissions within a seaport have significant effects on the urban environment. In order to reduce gas emissions, electrification of seaports has been a promising trend. Besides the integrated thermal network for cold-chain supply, the future seaport can be viewed as a transportation integrated energy system, and the coordination between the shipside and portside will highly influence the system behaviors of seaport. In this paper, the energy models of two basic ship-port coordination, i.e., on-shore power supply management (cold-ironing) and berth allocation are proposed, and an integrated energy system scheduling model is proposed to simultaneously meet the electric and thermal power demands. The port of Houston is set as the test case to show the validity of proposed method and the simulation results demonstrate berth allocation can provide adequate flexibility to facilitate the seaport operation.

SIMULATION OF VESSEL BERTHING USING ARENA: CASE OF BEIRUT CONTAINER TERMINAL

The main objective of this research is to analyze the current situation of Beirut Container Terminal. The proposed methodology is to mimic current terminal operations using a simulation model using ARENA software in order to identify causes of queueing occurring at berth allocation. Field research was conducted and both qualitative and quantitate data were collected using interviews, on–site observations, and online vessel tracking. A base model is developed to simulate the current operations at Beirut Container Terminal. Then, three different feasible scenarios are proposed to minimize the total time spent by the vessel at the quay side. Proposed scenarios take into consideration physical and resources expansion subject to political and financial constraints. The aim of this research is to provide a tool for the decision maker at Beirut Container Terminal in formulating an investment strategy for future expansion.

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.

Berth Allocation Model for Container Terminal using Genetic Algorithm Technique: Case of Apapa Wharf, Lagos, Nigeria

The Nigeria ports plays a vital role in socio-economic growth by being a cheap mode of conveying shipments for importation and exportation. The number of vessels coming into the Nigerian ports every year is on the average of about 4,900. A well flourishing and efficient ports and cargo management will in no doubt put a developing economy such as Nigeria in a leading pedestal with developed nations. Thus, stakeholders in container terminals are concerned about discharging containers as fast as possible, with the purpose of saving terminal costs. This study is driven to minimize the time being used up by ships in container terminal using genetic algorithm (GA) and thus attain maximum efficiency. The limited berth space in the wharf lead to berth allocation problem (BAP) and an optimal solution is required. Moreover, high berth occupancy results in congestion where vessels are queuing to be served. This leads to high turn-around time and results in bad service for the container terminal. The aim of this study is to develop and implement a genetic algorithm based model for berth allocation (i.e. GAMBA) with the view to minimize the total delay times of vessels at container terminals. A study of the operations in Apapa wharf was done with the view to understand the berth allocation process vis-à-vis the challenges therein. The relevant parameters required for berth allocation were identified and GAMBA was developed using the identified parameters. GAMBA was implemented using real life data collected from the container terminal, Apapa, Lagos, Nigeria. The results showed that increasing the quay length by 250m has a very similar outcome on the container port’s efficiency as reducing the proportion of increasing handling time by 0.0025 h/m. This revealed that the outcome on the container port’s efficiency by increasing the quayside length was the same as reducing the proportion of increasing management time. Based on these results, the optimized allocation of container storage and the automation of the handling process can be proposed as cheaper alternatives to construction and development of the containers port in relation to increasing the productivity of the port.

An Evaluation of First-Come, First-Served Scheduling in a Geometrically-Constrained Wet Bulk Terminal

In 2022, a new sea lock at IJmuiden is expected to open, permitting an increase in marine traffic of larger ships from the sea to the port. In the interest of facilitating operations, we evaluate the impact of the current first-come, first-served (FCFS) admittance policy in the context of berth allocation for a wet bulk terminal in the port. Four model types are constructed: optimal FCFS; no FCFS with fixed arrival times; 48-h arrival time relaxation; and complete arrival time relaxation. Comparison of the model types is done by means of a rolling time window: of each day within the time frame, a schedule was created for the following 2 weeks, after which the objective value was calculated. When comparing the average of all objective values, it was found that the optimal FCFS model already shows an improvement compared to the historical situation. Between the FCFS and the no FCFS model, there are no considerable differences, because the vessels are constrained to be scheduled on/after their arrival time at the port. When relaxation is allowed, a considerable efficiency gain is possible, especially if larger ships arrive at the port.

Berth assignment for liner carrier clusters under a cooperative environment

The berth allocation problem (BAP) has been extensively studied in the literature, which seeks to determine berths and times for upcoming ships. The previous studies do not distinguish berths and ships among different liner carriers. By taking into account the increasing use of dedicated berths in practice, this paper proposes a special BAP where berths and ships could be distinguished among different liner carriers. For our BAP under a cooperative environment, we propose a mixed-integer programming model. Based on an assignment model with binary variables, our BAP can be reduced to the liner carrier clustering problem. To generate stable liner carrier clusters used in our assignment model, queueing theory and core theory are applied. A three-stage optimization method is proposed to solve our BAP. Namely, stage I proposes a queueing theory approach for various liner carrier clusters, and based on core theory stage II determines the stability of different liner carrier clusters. Based on the results obtained in stages I and II, stage III solves the liner carrier clustering problem. Numerical experiments are carried out to demonstrate the effectiveness of the proposed method.

Particle swarm optimization algorithm with time buffer insertion for robust berth scheduling

This paper investigates the robust berth allocation problem in container terminals. To handle the uncertainties in vessel arrivals, the problem is formulated as a scenario-based two-stage stochastic programming model. Furthermore, we introduce the time buffers to the model. We then develop an algorithm for time buffer insertion, which accommodates the adaptive search procedure for the time buffer into the Particle Swarm Optimization (PSO) algorithm. Different from the traditional PSO algorithm, a core operator is designed with a modified version to take the intelligent time buffer insertion approach. The results of the numerical experiments show that the proposed method consistently provides an improved performance in terms of solution quality, compared with the previous studies and the one with a traditional operator in the PSO algorithm.

An integrated model for vessel traffic and deballasting scheduling in coal export terminals

Most bulk carriers arrive at coal export terminals (CETs) under empty or lightly loaded conditions. Ballast water, which is onboarded to maintain vessel stability during a voyage, must be pumped out as the cargo is loaded. As modern CETs are rapidly developing, vessels’ deballasting capability is being updated too slowly and far behind the CET loading efficiency improvement. Therefore, an increasing number of vessels fail to finish deballasting in a timely manner, which results in frequent operational interruptions at the terminal. The high loading efficiency of modern CETs is discounted by a ship’s deballasting capability. Conventionally, many CETs have allowed vessels to deballast in advance at anchorages or lay-by berths to avoid delays at the loading berth and wasted resources. In this sense, in addition to making a complex traffic-scheduling plan for vessels entering and leaving terminals, port managers must make proper deballasting plans for vessels and avoid excessive deballasting problems. However, research on vessel scheduling considering deballasting is limited to date. To fill this gap, this paper proposes an integrated scheduling model (ISM) that manages the scheduling process of vessel traffic and deballasting operations. Special considerations of vessel stability under deballasting and tidal influence are introduced. As a result, with the goal of minimizing the total weighted delay, the model can simultaneously optimize the vessel sequence, lay-by berth allocation, and deballasting plan. A series of comprehensive experiments was conducted based on practical data collected from a major CET in China. The performance of the proposed ISM was tested through comparisons of scheduling under common rule-based scheduling methods with varying traffic density and terminal efficiency properties. The results show that the ISM can avoid untimely deballasting, shorten the vessel port stay time, and directly contribute to a total weighted delay reduction of 20.84%. Application of the ISM to address the conflict of unbalanced machinery upgrades between CETs and vessels at various stages is of great importance to guarantee improved port efficiency.

A New Memetic Approach to Solve the Strategic Berth Template Problem

The berth allocation problem is one of the main concerns of port operators at a container terminal. In this paper, the authors study the berth allocation problem at the strategic level commonly known as the strategic berth template problem (SBTP). This problem aims to find the best berth template for a set of calling ships accepted to be served at the port. At strategic level, port operator can reject some ships to be served for avoid congestion. Since the computational complexity of the mathematical formulation proposed for SBTP, solution approaches presented so far for the problem are limited especially at level of large-scale instances. In order to find high quality solutions with a short computational time, this work proposes a population based memetic algorithm which combine a first-come-first-served (FCFS) technique, two genetics operators, and a simulating annealing algorithm. Different computational experiences and comparisons against the best known solutions so far have been presented to show the performance and effectiveness of the proposed method.

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

Berth Allocation in Transshipment Ports by Considering Quay Crane Coverage and Ship Fuel Consumption

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.

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.

Berth allocation problem with uncertain vessel handling times considering weather conditions

The vessel handling times at container terminals can be inevitably affected by weather conditions. This study proposes a berth allocation problem (BAP) with vessel handling time uncertainty considering the impact of weather conditions, which is seldom considered in the previous studies on BAPs. A two-stage optimization method, which focuses on the evaluation of vessel handling time under different weather conditions, is developed for addressing our BAP. In stage I, we determine the vessel handling times considering the influence of weather conditions. Based on the vessel handling times obtained in stage I, stage II presents a mixed-integer programming (MIP) model for solving the BAP. Moreover, an efficient particle swarm optimization algorithm embedded with machine learning approach is devised for solving the BAP model in large-scale problem cases. Numerical experiments are carried out to assess the effectiveness of the proposed model and the efficiency of the proposed algorithm.