Integrated Scheduling of Vessels, Cranes and Trains to Minimize Delays in a Seaport Container Terminal

The multiple processes taking place on a daily basis at an intermodal container terminal are often considered individually, given the complexity of their joint consideration. Nevertheless, the integrated planning and scheduling of operations in an intermodal terminal, including the arrivals and departures of trains and vessels, is a very relevant topic for terminal managers, which can benefit from the application of Operations Research (OR) techniques to obtain near-optimal solutions without excessive computational cost. Applying the functional integration technique, we present here a mathematical model for this terminal planning process, and solve it using heuristic procedures, given its complexity and size. Details on the benchmark comparison of a genetic algorithm, a simulated annealing routine and a tabu search are provided for different problem instances

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

Simulation framework of port operation and recovery planning

This study proposes a framework of simulation tool suites for ports to evaluate their response to disaster crisis and port security policies. The focus is containerized cargos that are imported through ports in the U.S. with final destinations also in the U.S. A crisis, such as a man-made or natural disaster, may cause a delay at the seaport. The down time of ports may result in severe economic losses. Thus, when a seaport cannot normally operate, it is important to minimize the impact caused by the disrupted freight flow. Port security policies also have a significant impact on the port operation efficiency. This model developed in this study evaluates the performance of re-routing strategies under different crisis scenarios and can help the user to find an effective re-routing decision and analyze security policies of a port. This model also analyzes security policies of the simulation port

Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching

In a marine container terminal, truck dispatching is a crucial problem that impacts on the operation efficiency of the whole port. Traditionally, this problem is formulated as an offline optimisation problem, whose solutions are, however, impractical for most real-world scenarios primarily because of the uncertainties of dynamic events in both yard operations and seaside loading–unloading operations. These solutions are either unattractive or infeasible to execute. Herein, for more intelligent handling of these uncertainties and dynamics, a novel cooperative double-layer genetic programming hyper-heuristic (CD-GPHH) is proposed to tackle this challenging online optimisation problem. In this new CD-GPHH, a novel scenario genetic programming (GP) approach is added on top of a traditional GP method that chooses among different GP heuristics for different scenarios to facilitate optimised truck dispatching. In contrast to traditional arithmetic GP (AGP) and GP with logic operators (LGP) which only evolve on one population, our CD-GPHH method separates the scenario and the calculation into two populations, which improved the quality of solutions in multi-scenario problems while reducing the search space. Experimental results show that our CD-GPHH dominates AGP and LGP in solving a multi-scenario function fitting problem as well as a truck dispatching problem in container terminal

Simulation study of different yard and crane side operations at a container terminal

With the expected growing in maritime container terminal and the exponential growth in vessels size, container terminals operations are facing higher demands. Being able to simulate the operations that occur inside container terminals can lead to obtain valuable insights, help to identify possible problems and solve them, and also some decision-making tools can be created. This thesis, then, uses simulation to model the operations of a container terminal in Denmark. The movements of containers from the storage area to the quay crane for loading and from the quay crane to the storage area for unloading are modelled. The simulation software used is AnyLogic. Once the model is created, an explanation of it and its limitations are presented. The databases used to run the simulation are explained and a validation of the model is performed with real data. After that, a probabilistic function to generate new data is found in order to run simulations of future vessels. Four different vessels are analysed with real data and generated data. A comparison of the results is made and some conclusions are extracted. To end, some recommendations for future work are presented.Outgoin

Integrated vehicle dispatching for container terminal

Ph.DDOCTOR OF PHILOSOPH