70 research outputs found

    Sea Container Terminals

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    Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

    Hierarchical modeling and analysis of container terminal operations

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    After the breakdown of trade barriers among countries, the volume of international trade has grown significantly in the last decade. This explosive growth in international trade has increased the importance of marine transportation which constitutes the major part of the global logistics network. The utilization of containers and container ships in marine transportation has also increased after the eighties due to various advantages such as packaging, flexibility, and reliability. Parallel to the container throughput, the capacities of ships and sizes of fleets as well as the number of terminals have been increased considerably. Substantial pressure of competition on ship operators and terminal managers has forced them to consider the issues regarding operational efficiency more deeply. Thus, the operational efficiency at port container terminals has become the major concern of terminal managers to satisfy the rapid transshipment of goods. In this thesis, we focus on a set of decision problems regarding container terminal operations. Since these problems are interrelated hierarchically, we attempt to model and analyze them consecutively. First, we consider the storage space allocation problem over a rolling horizon as an aggregate planning model. Since the model has the minimum cost flow network structure there exist polynomial time solution procedures via linear programming models. Although ship turnaround time is the principal performance criteria for whole container terminal operations, the total distances traveled by containers in the terminal throughout the planning horizon is determined as the surrogate objective function for the allocation model. The output of the storage space allocation problem is used as the input for the next step of our methodology, namely the location matching model. With the location matching model, the routes of vehicles for each time period have been identified while minimizing the total distance traveled by the vehicles, which reveals the ship turnaround times. The routes that are found subject to the output of storage space allocation models are better than those of random allocation in terms of total distances traveled. Next, the vehicle scheduling problem is discussed for different levels of complexity. The solution procedures proposed for similar problems in the machine scheduling literature are provided. Finally, we discuss the problem of simultaneous vehicle dispatching with precedence constraints. We have modeled the problem as a nonlinear mixed integer programming model and proposed an iterative solution procedure to obtain reasonable solutions in considerable times. Moreover, we have presented the worst-case performance analysis for this heuristic

    A framework for generating efficient yard plans for marine container terminals

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    Ph.DDOCTOR OF PHILOSOPH

    Research into container reshuffling and stacking problems in container terminal yards

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    Container stacking and reshuffling are important issues in the management of operations in a container terminal. Minimizing the number of reshuffles can increase productivity of the yard cranes and the efficiency of the terminal. In this research, the authors improve the existing static reshuffling model, develop five effective heuristics, and analyze the performance of these algorithms. A discrete-event simulation model is developed to animate the stacking, retrieving, and reshuffling operations and to test the performance of the proposed heuristics and their extended versions in a dynamic environment with arrivals and retrievals of containers. The experimental results for the static problem show that the improved model can solve the reshuffling problem more quickly than the existing model and the proposed extended heuristics are superior to the existing ones. The experimental results for the dynamic problem show that the results of the extended versions of the five proposed heuristics are superior or similar to the best results of the existing heuristics and consume very little time

    Solving the pre-marshalling problem to optimality with A* and IDA*

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

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    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

    Space-sharing Strategies for Storage Yard Management in a Transshipment Hub Port

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    Ph.DDOCTOR OF PHILOSOPH
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