A study of Intelligent Transport Systems (ITS) in Dublin Port in conjunction with the Intelligent Transport for Dynamic Environment (InTraDE) Project

In the last four decades the container as an essential part of a unit load-concept has achieved authentic importance in international sea freight transportation. With ever increasing containerization the number of port container terminals and competition among them has become quite remarkable. Port container operations are nowadays unthinkable without effective and efficient use of Intelligent Transport Systems (ITS) (Steenken & Stahlbock, 2004). The main problem in handling increasing levels of cargo is managing the internal traffic and optimizing space inside smaller and medium sized ports. A gap exists between automated cargo handling equipment that is suitable for use in the larger container terminals such as Rotterdam and its suitability in smaller terminals such as Dublin. A new generation of cargo handling technology has been designed in the form of an Intelligent Autonomous Vehicle (IAV). The IAV is a clean, safe, intelligent vehicle which will contribute to improving the traffic management and space optimization inside confined space by developing a clean, safe and intelligent transport system. This technology has been designed and developed as part of the ‘InTraDE’ (Intelligent Transport for Dynamic Environment) project to which the research has contributed. By using ITSs, logistics operations could be improved by enhancing the exchange of information and real-time status updates regarding different business operations in different modes of transportation (Schumacher et al., 2011). Maritime transport has recently gained increased attention, especially in connection to the building and further development of ITS (Pietrzykowski, 2010). This research looks at the main logistic processes and operations in port container terminals. It discusses the extent to which the terminal shipping operators in Dublin Port currently meet the demands of their customers and whether the introduction of ITS could enhance the efficiency and productivity of such services

An improved memetic algorithm to enhance the sustainability and reliability of transport in container terminals

This paper improves our previous attempts in which we studied a combination of an evolutionary algorithm (EA) and Monte Carlo simulation (MCS). Results of those studies showed the process of sampling in MCS is very time consuming. This prevents the EA from producing an accurate estimation of the robust solutions within reasonable time. Thus the present work improves the performance of the EA to make it possible to reach high quality solutions in reasonable time, therefore yielding a number of more practical solutions in real cases. Firstly, it proposes a new sampling technique to generate samples that better reflect the worst-case scenarios. This helps the EA to find more robust solutions using smaller sample sizes. Secondly, it proposes a new adaptive sampling technique to adjust the sample size during evolution. Subsequently, to evaluate the proposed algorithm we tested it in a typical environment with shuttle transport tasks: container terminal. Experimental results show that such improvements led to a significantly improved performance of the EA, thus making the proposed algorithm perfectly usable for empirical cases

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

Synchromodal logistics: An overview of critical success factors, enabling technologies, and open research issues

Abstract As supply chain management is becoming demand driven, logistics service providers need to use real-time information efficiently and integrate new technologies into their business. Synchromodal logistics has emerged recently to improve flexibility in supply chains, cooperation among stakeholders, and utilization of resources. We survey the existing scientific literature and real-life developments on synchromodality. We focus on the critical success factors of synchromodality and six categories of enabling technologies. We identify open research issues and propose the introduction of a new stakeholder, which takes on the role of orchestrator to coordinate and provide services through a technology-based platform

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

Control of free-ranging automated guided vehicles in container terminals

Container terminal automation has come to the fore during the last 20 years to improve their efficiency. Whereas a high level of automation has already been achieved in vertical handling operations (stacking cranes), horizontal container transport still has disincentives to the adoption of automated guided vehicles (AGVs) due to a high degree of operational complexity of vehicles. This feature has led to the employment of simple AGV control techniques while hindering the vehicles to utilise their maximum operational capability. In AGV dispatching, vehicles cannot amend ongoing delivery assignments although they have yet to receive the corresponding containers. Therefore, better AGV allocation plans would be discarded that can only be achieved by task reassignment. Also, because of the adoption of predetermined guide paths, AGVs are forced to deploy a highly limited range of their movement abilities while increasing required travel distances for handling container delivery jobs. To handle the two main issues, an AGV dispatching model and a fleet trajectory planning algorithm are proposed. The dispatcher achieves job assignment flexibility by allowing AGVs towards to container origins to abandon their current duty and receive new tasks. The trajectory planner advances Dubins curves to suggest diverse optional paths per origin-destination pair. It also amends vehicular acceleration rates for resolving conflicts between AGVs. In both of the models, the framework of simulated annealing was applied to resolve inherent time complexity. To test and evaluate the sophisticated AGV control models for vehicle dispatching and fleet trajectory planning, a bespoke simulation model is also proposed. A series of simulation tests were performed based on a real container terminal with several performance indicators, and it is identified that the presented dispatcher outperforms conventional vehicle dispatching heuristics in AGV arrival delay time and setup travel time, and the fleet trajectory planner can suggest shorter paths than the corresponding Manhattan distances, especially with fewer AGVs.Open Acces

Evolutionary Algorithms and Simulation for Intelligent Autonomous Vehicles in Container Terminals

The study of applying soft computing techniques, such as evolutionary computation and simulation, to the deployment of intelligent autonomous vehicles (IAVs) in container terminals is the focus of this thesis. IAVs are a new type of intelligent vehicles designed for transportation of containers in container terminals. This thesis for the first time investigates how IAVs can be effectively accommodated in container terminals and how much the performance of container terminals can be improved when IAVs are being used. In an attempt to answer the above research questions, the thesis makes the following contributions: First, the thesis studies the fleet sizing problem in container terminals, an important design problem in container terminals. The contributions include proposing a novel evolutionary algorithm (with superior results to the state-of-the-art CPLEX solver), combining the proposed evolutionary algorithm with Monte Carlo simulation to take into account uncertainties, validating results of the uncertain case with a high fidelity simulation, proposing different robustness measures, comparing different robust solutions and proposing a dynamic sampling technique to improve the performance of the proposed evolutionary algorithm. Second, the thesis studies the impact of IAVs on container terminals’ performance and total cost, which are very important criteria in port equipment. The contributions include developing simulation models using realistic data (it is for the first time that the impact of IAVs on containers terminals is investigated using simulation models) and applying a cost model to the results of the simulation to estimate and compare the total cost of the case study with IAVs against existing trucks. Third, the thesis proposes a new framework for the simulations of container terminals. The contributions include developing a flexible simulation framework, providing a user library for users to create 3D simulation models using drag-and-drop features, and allowing users to easily incorporate their optimisation algorithms into their simulations