Integrated vehicle dispatching for container terminal

Ph.DDOCTOR OF PHILOSOPH

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

Developing New Methods for Efficient Container Stacking Operations

Containerized transportation has become an essential part of the intermodal freight transport. Millions of containers pass through container terminals on an annual basis. Handling a large number of containers arriving and leaving terminals by different modalities including the new mega-size ships significantly affects the performance of terminals. Container terminal operators are always looking for new technologies and smart solutions to maintain efficiency. They need to know how different operations at the terminal interact and affect the performance of the terminal as a whole. Among all operations, the stacking area is of special importance since almost every container must be stacked in this area for a period of time. If the stacking operations of the terminal are not well managed, then the response time of the terminal significantly increases and consequently the performance decreases. In this dissertation, we propose, develop, and test optimization methods to support the decisions of container terminal operators in the stacking area. First, we study how to sequence storage and retrieval containers to be carried out by a single or two automated stacking cranes in a block of containers. The objective is to minimize the makespan of the cranes. Finally, we study how to minimize the expected number of reshuffles when incoming containers have to be stacked in a block of containers. A reshuffle is the removal of a container stacked on top of a desired container. Reshuffling containers is one of the daily operations at a container terminal which is time consuming and increases a ship's berthing time

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

Container Handling Algorithms and Outbound Heavy Truck Movement Modeling for Seaport Container Transshipment Terminals

This research is divided into four main parts. The first part considers the basic block relocation problem (BRP) in which a set of shipping containers is retrieved using the minimum number of moves by a single gantry crane that handles cargo in the storage area in a container terminal. For this purpose a new algorithm called the look ahead algorithm has been created and tested. The look ahead algorithm is applicable under limited and unlimited stacking height conditions. The look ahead algorithm is compared to the existing algorithms in the literature. The experimental results show that the look ahead algorithm is more efficient than any other algorithm in the literature. The second part of this research considers an extension of the BRP called the block relocation problem with weights (BRP-W). The main goal is to minimize the total fuel consumption of the crane to retrieve all the containers in a bay and to minimize the movements of the heavy containers. The trolleying, hoisting, and lowering movements of the containers are explicitly considered in this part. The twelve parameters to quantify various preferences when moving individual containers are defined. Near-optimal values of the twelve parameters for different bay configurations are found using a genetic algorithm. The third part introduces a shipping cost model that can estimate the cost of shipping specific commodity groups using one freight transportation mode-trucking- from any origin to any destination inside the United States. The model can also be used to estimate general shipping costs for different economic sectors, with significant ramifications for public policy. The last part mimics heavy truck movements for shipping different kinds of containerized commodities between a container terminal and different facilities. The highly detailed cost model from part three is used to evaluate the effect of public policies on truckers\u27 route choices. In particular, the influence of time, distance, and tolls on truckers\u27 route selection is investigated.

Optimization-Based Simulation of Container Terminal Productivity using Yard Truck Double Cycling

ABSTRACT The growth of global trade transiting over the ocean has been continually increasing. A new generation of large vessels has recently been introduced to the transhipment system. These large vessels can carry more than 16000 twenty-foot equivalent container units (TEUs), maximizing shipping productivity. Container terminals must improve their productivity to meet the rapid increases in trade demand and to keep pace with developments in the shipbuilding industry. Reducing vessel turnaround time in container terminals increases the capacity for world trade. This time reduction can be achieved by improving one or more container terminal major resources or factors. The objective of this research is to maximize container terminal productivity by minimizing vessel turnaround time within reasonable hourly and unit costs. A new strategy is introduced, employing double cycling to reduce the empty travel of yard trucks. This double-cycling strategy still requires the use a single-cycle strategy before the trucks can be incorporated into double-cycle scheduling. The single-cycle start-up is necessary in order to create enough space to begin loading a vessel if there is no other space. The strategy is based on combining the efforts of two quay cranes (Unloading and Loading quay cranes) to work as a unit. The technique optimizes the number of trucks in terms of time and cost, minimizing yard truck cycles by minimizing single cycle routes and maximizing double cycle trips. This requires five steps. First, a good knowledge base of a container terminal’s operation and of the behaviours of the Quay cranes (QCs), Yard trucks, and Yard cranes needs to be constructed. Second, analysis of the collected data is required to simulate the container terminal operation and to implement the Genetic algorithm. Third, the double cycling truck strategy is simulated, tested and verified. Fourth, sensitivity analysis is performed to rank and select the best alternatives. Optimization of the selected alternatives in terms of productivity and cost as well as verifying the results using real case studies comprises the fifth step. Genetic Algorithm is used to optimize the results. Some selection approaches are implemented on the set of the nearest optimum solutions to rank and select the best alternative. The research offers immediate value by improving container terminal productivity using existing facilities and resources. Simulating the yard truck double cycling strategy provides container terminal mangers and decision makers with a clear overview of their handling container operations. Optimizing fleet size is a key factor in minimizing container handling costs and time. The simulation model reveals a productivity improvement of about 19% per QC. A reasonable cost savings in terms of the cost index in unit cost was achieved using yard truck double cycling operation. The genetic algorithm corroborates the achievements thus gained and determines the optimal fleet size that will result in the maximum terminal productivity (quickest vessel turnaround time) with the minimal cost. A time reduction of more than 26% was achieved in most cases, compared to previous research efforts

Containerbrückeneinsatzplanung in Seehafencontainerterminals - Entwurf und experimentelle Analyse von Lösungsverfahren für das Container Sequencing Problem

Durch den Einsatz immer größerer Containerschiffe zum Transport containerisierter Güter über den Seeweg gewinnt die Produktivität der zur Be- und Entladung der Containerschiffe eingesetzten Containerbrücken in Seehafencontainerterminals immer mehr an Bedeutung. Einen erheblichen Einfluss auf die Produktivität der Containerbrücken hat die Containerbrückeneinsatzplanung. Gegenstand dieser Arbeit ist das im Rahmen der Containerbrückeneinsatzplanung auftretende Container Sequencing Problem, welches hier erstmals unter Berücksichtigung von Ladelukendeckeln und Rehandlecontainern verschiedener Containerkategorien untersucht wird. Die Problemstellung wird als ganzzahliges lineares Optimierungsmodell formuliert. Zur Lösung des Problems werden verschiedene heuristische Verfahren vorgeschlagen. Deren Leistungsfähigkeit wird anhand numerischer Experimente analysiert