A combined Mixed Integer Programming model of seaside operations arising in container ports

This paper puts forward an integrated optimisation model that combines three distinct problems, namely the Berth Allocation Problem, the Quay Crane Assignment Problem, and the Quay Crane Scheduling problem, which have to be solved to carry out these seaside operations in container ports. Each one of these problems is complex to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence the need to solve them in a combined form. The problem is formulated as a mixed-integer programming model with the objective being to minimise the tardiness of vessels. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX

Simulation analysis for integrated container terminal activities

Analisi simualtiva delle attività  logistiche di un container terminal con utilizzo del software Arenaope

Optimal Energy Scheduling of a Solar-Based Hybrid Ship Considering Cold-Ironing Facilities

Abstract There are many restrictions on shipping, which reduce or prohibit the use of diesel generators for feeding the energy demand of the electric ships, especially in ports. Therefore, the use of shore power system (SPS) together with renewable energies and energy storage systems (ESSs) can lead to many environmental benefits while ships are berthing in ports. In this study, the shipboard hybrid power system (HPS) is proposed, including diesel generators, solar photovoltaic panels (PV), ESS and cold‐ironing (CI) facilities for using SPS to efficiently supply the ship's electrical demand. With such HPS aboard, the solar generated power is estimated accurately based on the navigation route. By optimal energy scheduling in a real hybrid cruise ship, the use of diesel generators gets minimised, due to the utilisation of PV and ESS. In addition, using CI service instead of switching on auxiliary diesel generators in ports leads to a 3 h increase in charging and discharging times of the ESS. Furthermore, the efficient use of CI service results in less use of diesel generators even at sailing hours, reducing emissions and minimising the costs of supplying ship's energy demand. The total cost reduction of the HPS in different case studies, without the use of CI services is only 1% to 2%, while this reduction is about 6% to 7% by adding the CI facilities to the HPS. Moreover, the economic characteristics of the proposed diesel‐PV‐ESS‐CI by adding the CI facilities to the HPS are analysed and the profitability of this HPS in reducing the daily costs with considering the share of installation costs on the target day is proved

An evolutionary approach to a combined mixed integer programming model of seaside operations as arise in container ports

This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports. Each one of these problems is difficult to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence, it is desirable to solve them in a combined form. The model is of the mixed-integer programming type with the objective being to minimize the tardiness of vessels and reduce the cost of berthing. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX. Large scale instances, however, can only be solved in reasonable times using heuristics. Here, an implementation of the genetic algorithm is considered. The effectiveness of this implementation is tested against CPLEX on small to medium size instances of the combined model. Larger size instances were also solved with the genetic algorithm, showing that this approach is capable of finding the optimal or near optimal solutions in realistic times

A Study on the Automatic Ship Control Based on Adaptive Neural Networks

Recently, dynamic models of marine ships are often required to design advanced control systems. In practice, the dynamics of marine ships are highly nonlinear and are affected by highly nonlinear, uncertain external disturbances. This results in parametric and structural uncertainties in the dynamic model, and requires the need for advanced robust control techniques. There are two fundamental control approaches to consider the uncertainty in the dynamic model: robust control and adaptive control. The robust control approach consists of designing a controller with a fixed structure that yields an acceptable performance over the full range of process variations. On the other hand, the adaptive control approach is to design a controller that can adapt itself to the process uncertainties in such a way that adequate control performance is guaranteed. In adaptive control, one of the common assumptions is that the dynamic model is linearly parameterizable with a fixed dynamic structure. Based on this assumption, unknown or slowly varying parameters are found adaptively. However, structural uncertainty is not considered in the existing control techniques. To cope with the nonlinear and uncertain natures of the controlled ships, an adaptive neural network (NN) control technique is developed in this thesis. The developed neural network controller (NNC) is based on the adaptive neural network by adaptive interaction (ANNAI). To enhance the adaptability of the NNC, an algorithm for automatic selection of its parameters at every control cycle is introduced. The proposed ANNAI controller is then modified and applied to some ship control problems. Firstly, an ANNAI-based heading control system for ship is proposed. The performance of the ANNAI-based heading control system in course-keeping and turning control is simulated on a mathematical ship model using computer. For comparison, a NN heading control system using conventional backpropagation (BP) training methods is also designed and simulated in similar situations. The improvements of ANNAI-based heading control system compared to the conventional BP one are discussed. Secondly, an adaptive ANNAI-based track control system for ship is developed by upgrading the proposed ANNAI controller and combining with Line-of-Sight (LOS) guidance algorithm. The off-track distance from ship position to the intended track is included in learning process of the ANNAI controller. This modification results in an adaptive NN track control system which can adapt with the unpredictable change of external disturbances. The performance of the ANNAI-based track control system is then demonstrated by computer simulations under the influence of external disturbances. Thirdly, another application of the ANNAI controller is presented. The ANNAI controller is modified to control ship heading and speed in low-speed maneuvering of ship. Being combined with a proposed berthing guidance algorithm, the ANNAI controller becomes an automatic berthing control system. The computer simulations using model of a container ship are carried out and shows good performance. Lastly, a hybrid neural adaptive controller which is independent of the exact mathematical model of ship is designed for dynamic positioning (DP) control. The ANNAI controllers are used in parallel with a conventional proportional-derivative (PD) controller to adaptively compensate for the environmental effects and minimize positioning as well as tracking error. The control law is simulated on a multi-purpose supply ship. The results are found to be encouraging and show the potential advantages of the neural-control scheme.1. Introduction = 1 1.1 Background and Motivations = 1 1.1.1 The History of Automatic Ship Control = 1 1.1.2 The Intelligent Control Systems = 2 1.2 Objectives and Summaries = 6 1.3 Original Distributions and Major Achievements = 7 1.4 Thesis Organization = 8 2. Adaptive Neural Network by Adaptive Interaction = 9 2.1 Introduction = 9 2.2 Adaptive Neural Network by Adaptive Interaction = 11 2.2.1 Direct Neural Network Control Applications = 11 2.2.2 Description of the ANNAI Controller = 13 2.3 Training Method of the ANNAI Controller = 17 2.3.1 Intensive BP Training = 17 2.3.2 Moderate BP Training = 17 2.3.3 Training Method of the ANNAI Controller = 18 3. ANNAI-based Heading Control System = 21 3.1 Introduction = 21 3.2 Heading Control System = 22 3.3 Simulation Results = 26 3.3.1 Fixed Values of n and = 28 3.3.2 With adaptation of n and r = 33 3.4 Conclusion = 39 4. ANNAI-based Track Control System = 41 4.1 Introduction = 41 4.2 Track Control System = 42 4.3 Simulation Results = 48 4.3.1 Modules for Guidance using MATLAB = 48 4.3.2 M-Maps Toolbox for MATLAB = 49 4.3.3 Ship Model = 50 4.3.4 External Disturbances and Noise = 50 4.3.5 Simulation Results = 51 4.4 Conclusion = 55 5. ANNAI-based Berthing Control System = 57 5.1 Introduction = 57 5.2 Berthing Control System = 58 5.2.1 Control of Ship Heading = 59 5.2.2 Control of Ship Speed = 61 5.2.3 Berthing Guidance Algorithm = 63 5.3 Simulation Results = 66 5.3.1 Simulation Setup = 66 5.3.2 Simulation Results and Discussions = 67 5.4 Conclusion = 79 6. ANNAI-based Dynamic Positioning System = 80 6.1 Introduction = 80 6.2 Dynamic Positioning System = 81 6.2.1 Station-keeping Control = 82 6.2.2 Low-speed Maneuvering Control = 86 6.3 Simulation Results = 88 6.3.1 Station-keeping = 89 6.3.2 Low-speed Maneuvering = 92 6.4 Conclusion = 98 7. Conclusions and Recommendations = 100 7.1 Conclusion = 100 7.1.1 ANNAI Controller = 100 7.1.2 Heading Control System = 101 7.1.3 Track Control System = 101 7.1.4 Berthing Control System = 102 7.1.5 Dynamic Positioning System = 102 7.2 Recommendations for Future Research = 103 References = 104 Appendixes A = 112 Appendixes B = 11

A Simulation-Based Optimization Approach for Integrated Port Resource Allocation Problem

Todays, due to the rapid increase in shipping volumes, the container terminals are faced with the challenge to cope with these increasing demands. To handle this challenge, it is crucial to use flexible and efficient optimization approach in order to decrease operating cost. In this paper, a simulation-based optimization approach is proposed to construct a near-optimal berth allocation plan integrated with a plan for tug assignment and for resolution of the quay crane re-allocation problem. The research challenges involve dealing with the uncertainty in arrival times of vessels as well as tidal variations. The effectiveness of the proposed evolutionary algorithm is tested on RAJAEE Port as a real case. According to the simulation result, it can be concluded that the objective function value is affected significantly by the arrival disruptions. The result also demonstrates the effectiveness of the proposed simulation-based optimization approach. </span

Application of Machine and Deep Learning to Mooring, Dynamic Positioning, and Ship Berthing Systems

In recent years, there have been a surge of advances in machine and deep learning due to accessibility to a large amount of digital data, developments in computer hardware, and state-of-the-art machine and deep learning algorithms proposed. The robust performance of the recent machine and deep learning algorithms have been proven in many applications such as natural language processing, computer vision, market research, self-driving car, autonomous shipping, and so on. The application of machine and deep learning is very powerful in a sense that one does not need to build such a complex and hard-coded system to implement sophisticated functionality. Instead, a machine and deep learning-based system can be trained on a collected training dataset and the trained system can robustly perform as desired. There are two main advantages of the use of machine and deep learning-based systems over the traditional hard-coded systems. First, as mentioned, the machine and deep learning-based systems do not require such complex and hard-coded algorithms, therefore, such learning systems are less prone to errors and faster to implement without much debugging. Second, the machine and deep learning-based systems can adapt to varying circumstances through re-training based on collected data. An example of the varying circumstance can be a varying purchase trend impacted by the media. Therefore, even if the input distribution from the circumstance changes over time, the machine and deep learning-based systems can easily adapt. In this paper, the machine and deep learning algorithms are applied to various applications such as a mooring system, dynamic positioning system (DPS), and ship berthing system. Specifically, the machine and deep learning algorithms are utilized to build a mooring line tension prediction system, a feed-forward system for DPS, an adaptive proportional-integral-derivative (PID) controller for DPS, and an automatic ship berthing system.1. Introduction 1 2. Background of Machine and Deep Learning 4 2.1 Machine Learning 4 2.2 Deep Learning 9 2.2.1 Types of Deep Learning Layers 9 2.2.2 Activation Function and Weight Initialization Methods 18 2.2.3 Optimizers 19 2.2.4 Training Dataset Scaling 26 2.2.5 Transfer Learning 28 2.3 Reinforcement Learning 28 3. Machine Learning-Based Mooring Line Tension Prediction System 39 3.1 Introduction 39 3.2 Brief Comparison Between Conventional and Proposed Mooring Line Tension Prediction Systems 40 3.3 Proposed K-Means-Based Sea State Selection Method 41 3.3.1 Padding 42 3.3.2 K-Means 44 3.3.3 K-Means-Based Monte Carlo Method 45 3.3.4 Feature Vector Generation 47 3.3.5 Clustering of Relevant Sampled Sea States with K-Means 48 3.4 Proposed Hybrid Neural Network Architecture 50 3.4.1 Architecture 50 3.4.2 Training Procedure 54 3.5 Simulation and Result Discussion 55 3.5.1 Simulation Conditions 55 3.5.2 Overall Hs-focused NN model 56 3.5.3 Effectiveness of Batch Normalization 59 3.5.4 Low Hs-focused NN model 60 3.5.5 Proposed Hybrid Neural Network Architecture 61 4. Motion Predictive Control for DPS Using Predicted Drifted Ship Position Based on Deep Learning and Replay Buffer 65 4.1 Introduction 65 4.2 PID Feed-Back System and Wind Feed-Forward System 66 4.3 Proposed Motion Predictive Control 69 4.4 Numerical Modeling of Target Ship's Behavior 73 4.4.1 Target Ship and DPS 73 4.4.2 Equation of Motion of Target Ship 74 4.5 Effectiveness of Proposed Algorithms 76 4.5.1 Simulation Conditions 76 4.5.2 Types of Deep Learning Layers 77 4.5.3 Real-Time Normalization Method 78 4.5.4 Replay Buffer 80 4.6 Simulation and Result Discussion 81 4.6.1 Simulation Under One Environmental Condition 81 4.6.2 Simulation Under Two Different Sequential Environmental Conditions 84 5. Reinforcement Learning-Based Adaptive PID Controller for DPS 88 5.1 Introduction 88 5.2 Target Ship and DPS 90 5.2.1 PID Control in DPS 91 5.2.2 Hydrodynamics Associated with a Drifting Motion of a Ship 93 5.3 Proposed Adaptive Fine-Tuning System for PID Gains in DPS 95 5.4 Simulation Results 99 5.4.1 Effectiveness of the Proposed Adaptive Fine-Tuning System 99 5.4.2 Overall Performance Assessment 103 5.5 Discussion 107 6. Application of Recent Developments in Deep Learning To ANN-based Automatic Berthing System 111 6.1 Introduction 111 6.2 Mathematical Model of Ship Maneuvering 112 6.2.1 Mathematical Model for Ship-Maneuvering Problem 113 6.2.2 Modeling of Propeller and Rudder 114 6.3 Artificial Neural Network and Important Factors in Training the Network 115 6.3.1 Artificial Neural Network 115 6.3.2 Optimizer 117 6.3.3 Input Data Scaling 117 6.3.4 Number of Hidden Layers 118 6.3.5 Overfitting Prevention 118 6.4 Application of Recent Developments in Deep Learning to Automatic Berthing 119 6.5 Simulation and Result Discussion 125 7. Conclusion 131 7.1 Machine Learning-Based Mooring Line Tension Prediction System 131 7.2 Motion Predictive Control for DPS Using Predicted Drifted Ship Position Based on Deep Learning and Replay Buffer 132 7.3 Reinforcement Learning-Based Adaptive PID Controller for DPS 133 7.4 Application of Recent Developments in Deep Learning to ANN-Based Automatic Berthing System 134Maste