Neural Network Assisted Genetic Programming in Dynamic Container Port Truck Dispatching

Understanding the economic theory that underpins container port production is a prerequisite for policy making or taking action in order to improve the performance of a container port. This is not an easy task, however, because of the complexity of container port production. This complexity is not only related to container port operations and their management, but also to the macroeconomic environment and market structure under which container ports operate. This chapter sets out to describe the economic functions of a port and the challenges for contemporary container ports arising from the complicated nature of their operations and the challenging market demands of port users and other market players.

CPDGen: a Scalable Synthetic Dataset Generator for Container Port Operations

Graph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behaviour of Cartesian genetic programming (CGP), linear genetic programming (LGP), evolving graphs by graph programming and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and (1+lambda ). In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods can increase search performance on complex real-world regression problems and, particularly in combination with the (1 + lambda) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP’s number of registers and CGP’s levels back parameter is of utmost importance and contributes significantly to better convergence of an optimization algorithm when solving complex problems that benefit from code reuse.

Container terminals require continuous benchmarking of competitiveness factors. This paper discusses factors of competitiveness with a main focus on feeder and mixed terminals (in which both feeder and transshipment services are provided). Method statement includes an economic literature review with respect to competitiveness factors and a container port expert survey process to achieve a list of most important factors among global container port operators. Experts were selected from different countries and private container terminals. A questionnaire was developed and sent online to them. Questions required both a ranking of factors as well as assigning monetary values to critical variables. Responses were analyzed using a pre-established strategy. Overall, experts ranked first the role of market related competitiveness factors related to service quality, price level and adaptability to the changing market environment. Intermodal links and general hinterland connections factors were more often mentioned than factors relating to nautical accessibility or other factors. Both assignment of monetary values as well as ranking have produced mutually coherent results. The paper does not claim to be definitive; it aims to provide an update of competitive factors hierarchy and highlights issues for further port development.

In port environments, container stacking at significant heights obstructs satellite signal reception by terminal equipment on container trucks, leading to inaccurate positional tracking data. To address this, it is necessary to predict container truck trajectories to fill in the inaccurate positioning signals. In this study, we collected port container truck trajectory data and compared the predictive performance of long short-term memory (LSTM) networks and transformer models, revealing performance turning points at different prediction steps. Based on these findings, we propose a hybrid model named CT-HybridNet, which integrates the LSTM-based DeepPBM-M model with the transformer-based PatchTST model. Given the independence of the prediction errors of the two models, we assume both errors follow a Gaussian distribution. By performing an affine transformation, the proposed hybrid method’s output also follows a Gaussian distribution. Additionally, an adaptive parameter adjustment mechanism optimises performance, enabling CT-HybridNet to achieve dual improvements in trajectory prediction accuracy and stability, with 15% improvement in short-term accuracy and 20% in long-term performance. This study provides a more accurate and stable technical solution for port container truck trajectory prediction, overcoming issues related to positioning inaccuracies and signal obstructions.

Route optimization for autonomous container truck is one of the key problems to realize the automatic container port. An environment model for container truck is built by grid method. Considering the complex and unknown construction environment of the container port, an improved ant colony optimization (IACO) algorithm based on rolling window is proposed to achieve path planning for container truck. In the simulations, it is obvious that the IACO will not only achieve a safe collision avoidance path, the length of which is shorter than the truck's traditional route no matter how complex the environment is, but also show good analytical and disposing ability of dead ends in the path planning process. Compared with conventional ant colony optimization (ACO), the running time of IACO is shorter. The results of the simulation experiments demonstrate that the IACO is a good method which is applicable to route optimization for autonomous container truck.

A Review of Wind Impact on Container Port Operations: Current Technologies, Strategies and Future Perspectives

The stacking problem is a hard combinatorial optimization problem with high practical interest in, for example, steel storage or container port operations. In this problem, a set of items is stored in a warehouse for a period of time, and a crane is used to place them in a limited number of stacks. Since the entrance and exit of items occurs in an arbitrary order, items may have to be relocated in order to reach and deliver other items below them. The objective of the problem is to find a feasible sequence of movements that delivers all items, while minimizing the total number of movements. We study the scalability of an exact approach to this problem, and propose two heuristic methods to solve it approximately. The two heuristic approaches are a multiple simulation algorithm using semi-greedy construction heuristics, and a stochastic best-first tree search algorithm. The two methods are compared in a set of challenging instances, revealing a superior performance of the tree search approach in most cases.

Contents: Introduction, James Wang, Daniel Olivier, Theo Notteboom and Brian Slack. Part 1 Conceptualization of Port-Cities and Global Supply Chains: Supply chain and supply chain management: appropriate concepts for maritime studies, Valentina Carbone and Elisabeth Gouvernal Global supply chain integration and competitiveness of port terminals, Photis M. Panayides The terminalisation of seaports, Brian Slack Re-assessing port-hinterland relationships in the context of global supply chains, Theo Notteboom and Jean-Paul Rodrigue. Part 2 Shipping Networks and Port Development: The development of global container transhipment terminals, Alfred J. Baird Mediterranean ports in the global network: how to make the hub and spoke paradigm sustainable?, Enrico Musso and Francesco Parola Northern European range: shipping line concentration and port hierarchy, Antoine Fremont and Martin Soppe Factors influencing the landward movement of containers: the cases of Halifax and Vancouver, Robert J. McCalla. Part 3 Inserting Port-Cities into Global Supply Chains: Globalization and the port-urban interface: conflicts and opportunities, Yehuda Hayuth A metageography of port-city relationships, Cesar Ducruet Chinese port-cities in the global supply chains, James Wang and Daniel Olivier The economic performance of seaport regions, Peter W. De Langen. Part 4 Corporate Perspectives on the Insertion of Ports in Global Supply Chains: The success of Asian container port operators: the role of information technology, Daniel Olivier and Francesco Parola Which link in which chain? Inserting Durban into global automotive supply chains, Peter V. Hall and Glen Robbins Sustainable development and corporate strategies of the maritime industry, Claude Comtois and Brian Slack References Index.

With the development of the Shanghai International Shipping Center, the diversity of vehicle types on the highways and arterial roads near Yangshan port is continually increasing. Within such a container port corridor, large container trucks are primarily utilized for mainline transportation. Their larger size and significant inertia would increase psychological pressure on sedan drivers, and elevate their behavior risk. To investigate the effects of container trucks on drivers’ visual characteristics and driving behavior as well as to predict driving risk, firstly, this research conducted field tests in four scenarios surrounding the port. Visual characteristics and behavior data of sedan drivers were collected. Secondly, a “Visual‐behavior” chain model was established. The relationship between drivers’ visual characteristics, driving behavior, and driving risk was illustrated from the perspective of time‐series behavior patterns. Thirdly, three driving risk prediction models were built with Autoregressive Integrated Moving Average (ARIMA), Long Short‐Term Memory (LSTM), and ARIMA‐LSTM. The results indicate that the ARIMA‐LSTM model shows the most effective prediction performance. This research provides a field‐data comparative analysis of the driving risks influenced by a high proportion of container trucks. The findings contribute to understanding the unique mixed traffic visual environment around large‐scale container ports.

Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach

Activity recognition from smartphone sensor inputs is of great importance to enhance user experience. Our study aims to investigate the applicability of Genetic Programming (GP) approach on this complex real world problem. Traditional methods often require substantial human efforts to define good features. Moreover the optimal features for one type of activity may not be suitable for another. In comparison, our GP approach does not require such feature extraction process, hence, more suitable for complex activities where good features are difficult to be pre-defined. To facilitate this study we therefore propose a benchmark of activity data collected from various smartphone sensors, as currently there is no existing publicly available database for activity recognition. In this study, a GP-based approach is applied to nine types of activity recognition tasks by directly taking raw data instead of features. The effectiveness of this approach can be seen by the promising results. In addition our benchmark data provides a platform for other machine learning algorithms to evaluate their performance on activity recognition.

A major goal for port authorities, operators, and investors is to achieve efficient operations and effective environmental protection. This is because the environmental performance of a container port is important for its competitiveness and sustainable development. However, the container ports along the Maritime Silk Road (MSR) have caused numerous problems with the rapid development, among which the most significant problem is environmental pollution. In this paper, we aim to measure and compare the environmental performance and operational efficiency of ten major container ports along the MSR, including the ports of Shanghai, Hong Kong, Singapore, Kelang, Laem Chabang, Colombo, Dubai, Barcelona, Antwerp, and Hamburg. We develop an improved, inseparable data envelopment analysis (DEA) model with slack-based measures (SBMs) to evaluate and compare the environmental performance and operational efficiency, and we incorporate the desirable output of container throughput as well as the undesirable output of CO2 emission. Our results show that. Overall. these container ports perform better in terms of operational efficiency than environmental performance. We also provide insights for management and policy makers for container ports with different levels of operational efficiency and environmental performance.

<p>Symbolic regression (SR) is a function identification process, the task of which is to identify and express the relationship between the input and output variables in mathematical models. SR is named to emphasise its ability to find the structure and coefficients of the model simultaneously. Genetic Programming (GP) is an attractive and powerful technique for SR, since it does not require any predefined model and has a flexible representation. However, GP based SR generally has a poor generalisation ability which degrades its reliability and hampers its applications to science and real-world modeling. Therefore, this thesis aims to develop new GP approaches to SR that evolve/learn models exhibiting good generalisation ability. This thesis develops a novel feature selection method in GP for high-dimensional SR. Feature selection can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalisation ability. However, feature selection is seldom considered in GP for high-dimensional SR. The proposed new feature selection method utilises GP’s built-in feature selection ability and relies on permutation to detect the truly relevant features and discard irrelevant/noisy features. The results confirm the superiority of the proposed method over the other examined feature selection methods including random forests and decision trees on identifying the truly relevant features. Further analysis indicates that the models evolved by GP with the proposed feature selection method are more likely to contain only the truly relevant features and have better interpretability. To address the overfitting issue of GP when learning from a relatively small number of instances, this thesis proposes a new GP approach by incorporating structural risk minimisation (SRM), which is a framework to estimate the generalisation performance of models, into GP. The effectiveness of SRM highly depends on the accuracy of the Vapnik-Chervonenkis (VC) dimension measuring model complexity. This thesis significantly extends an experimental method (instead of theoretical estimation) to measure the VC-dimension of a mixture of linear and nonlinear regression models in GP for the first time. The experimental method has been conducted using uniform and non-uniform settings and provides reliable VC-dimension values. The results show that our methods have an impressively better generalisation gain and evolve more compact model, which have a much smaller behavioural difference from the target models than standard GP and GP with bootstrap, The proposed method using the optimised non-uniform setting further improves the one using the uniform setting. This thesis employs geometric semantic GP (GSGP) to tackle the unsatisfied generalisation performance of GP for SR when no overfitting occurs. It proposes three new angle-awareness driven geometric semantic operators (GSO) including selection, crossover and mutation to further explore the geometry of the semantic space to gain a greater generalisation improvement in GP for SR. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, to be resistant to overfitting. The results show that compared with two kinds of state-of-the-art GSOs, the proposed new GSOs not only drive the evolutionary process fitting the target semantics more efficiently but also significantly improve the generalisation performance. A further comparison on the evolved models shows that the new method generally produces simpler models with a much smaller size and containing important building blocks of the target models.</p>

The port of Shanghai, as the world’s largest container port, has been experiencing rapid development in recent years, with increasing cargo throughput capacity. The combustion of diesel fuels used by internal and external port-related container trucks and in-port machineries can release various pollutants, causing air pollution. The terminals are close to the residential area, and the emissions are concentrated, which is worth paying attention to. This study aims to synthetically assess the port-related emissions and their environmental impacts. We firstly constructed an emission inventory of air pollutants in the port of Shanghai and then used the WRF-CMAQ model to estimate the influence of port-related source emissions on air quality. The results show that the annual emissions of SO2, NOX, CO, VOCS, PM, PM10, PM2.5, CO2, BC and OC caused by cargo-handling equipment were 21.88 t, 1811.22 t, 1741.72 t, 222.76 t, 61.52 t, 61.42 t, 58.41 t, 141,805.40 t, 26.80 t and 10.07 t in 2015. The emissions of NOX, CO, VOCS, PM10 and PM2.5 caused by external port-related container trucks were 18,002.92 t, 5308.0 t, 1134.57 t, 711.12 t and 640.58 t. The exhaust of external port-related container trucks was much larger than that of cargo-handling equipment, so the impact on air quality was also higher than that of the machinery. The peak annual average concentrations of PM2.5 and NOX contributed by the port-related sources were 1.75 μg/m3 and 49.21 μg/m3, respectively, which accounted for 3.08% and 36.7%, respectively, of the simulated ambient concentrations by all the anthropogenic emissions in Shanghai. Our results imply that the emission control policy to reduce the combined port-related emissions, especially for the cargo-delivery transportation phase from port to city, is key for large coastal port cities such as Shanghai.