Sequence-Based Simulation-Optimization Framework With Application to Port Operations at Multimodal Container Terminals

It is evident in previous works that operations research and mathematical algorithms can provide optimal or near-optimal solutions, whereas simulation models can aid in predicting and studying the behavior of systems over time and monitor performance under stochastic and uncertain circumstances. Given the intensive computational effort that simulation optimization methods impose, especially for large and complex systems like container terminals, a favorable approach is to reduce the search space to decrease the amount of computation. A maritime port can consist of multiple terminals with specific functionalities and specialized equipment. A container terminal is one of several facilities in a port that involves numerous resources and entities. It is also where containers are stored and transported, making the container terminal a complex system. Problems such as berth allocation, quay and yard crane scheduling and assignment, storage yard layout configuration, container re-handling, customs and security, and risk analysis become particularly challenging. Discrete-event simulation (DES) models are typically developed for complex and stochastic systems such as container terminals to study their behavior under different scenarios and circumstances. Simulation-optimization methods have emerged as an approach to find optimal values for input variables that maximize certain output metric(s) of the simulation. Various traditional and nontraditional approaches of simulation-optimization continue to be used to aid in decision making. In this dissertation, a novel framework for simulation-optimization is developed, implemented, and validated to study the influence of using a sequence (ordering) of decision variables (resource levels) for simulation-based optimization in resource allocation problems. This approach aims to reduce the computational effort of optimizing large simulations by breaking the simulation-optimization problem into stages. Since container terminals are complex stochastic systems consisting of different areas with detailed and critical functions that may affect the output, a platform that accurately simulates such a system can be of significant analytical benefit. To implement and validate the developed framework, a large-scale complex container terminal discrete-event simulation model was developed and validated based on a real system and then used as a testing platform for various hypothesized algorithms studied in this work

Efficient yard storage in transshipment container hub ports

Ph.DDOCTOR OF PHILOSOPH

Sea Container Terminals

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

Managing vessel arrival uncertainty in container terminals: a machine learning approach

A container terminal is a complex system where a broad range of operations are carried out involving a wide array of resources that need to interact over a 24 hour operating cycle. Since the various activities are mutually related to each other, there is a need not only to maximise the efficiency of each one, but also to ensure proper coordination, hence to solve integrated decision-making problems. Several factors can affect the quality of the services provided and the overall efficiency. Vessel arrival uncertainty further complicates the task of the planners and, as a result, of the effectiveness of the planning itself, in particular at the operational level. Each arrival produces high peak loads for other terminal activities, as well as for the supporting arrival activities (pilotage, towage, etc.) and hinterland transportation (waiting, congestion etc.). Deviating arrivals only worsen this peak load. On a daily level, the actual time of arrival of the vessels often deviates from the scheduled time. Despite contractual obligations to notify the Estimated Time of Arrival (ETA) at least 24 hours before the arrival, ship operators often have to adapt and update the latest ETA due to unexpected circumstances. This aspect results in a last-minute change of plans in terminal operations resulting in higher costs. In fact, the ability to predict the actual time of a vessel’s arrival in a port 24 hours in advance is fundamental for the related planning activities for which the decision-making processes need to be constantly adapted and updated. Moreover, disruptions in container flows and operations caused by vessel arrival uncertainty can have cascade effects within the overall supply chain and network within which the port is part. Although vessel arrival uncertainty in ports is a well-known problem for the scientific community, the literature review highlights that in the maritime sector the specific instruments for dealing with this problem are extremely limited. The absence of a reference model that specifies the relationship between vessel arrival uncertainty and the involved variables resulted in the application of a specific machine learning approach within the Knowledge Discovery in Database process. This V approach, that abandons all prior assumptions about data distribution shape, is based on the self-learning concept according to which the relation between an outcome variable Y and the set of predictors X is directly identified from the historical collected data. The approach has been validated thanks to two different case studies: the container terminal of Cagliari, located in the Mediterranean basin, and one of the main container terminals of Antwerp, located at the North Sea. Depending on the framework and planning purposes several estimates can provide useful information on vessel arrivals. Sometimes, it can be useful for planners to infer a quantitative estimate of the delay/advance in minutes, sometimes it may be useful to have a qualitative estimate, even only knowing whether or not an incoming vessel is likely to arrive before or after the scheduled ETA. For this reason a two-step instrument is proposed is made up of two different modules. The fitted algorithmic models used to obtain predictions are Logistic Regression, CART (Classification and Regression Trees) and Random Forest. All the proposed models are able to learn from experience, following the well-known Data Mining paradigm “learning from data”. From a practical point of view, the probability, associated to the continuous estimation, of specifically identifying the work-shift of the incoming vessel is calculated. In all predictions Random Forest algorithms still show the best performance. This aspect can help planners, in the daily strategy decision making process, in order to improve the use of the human, mechanical and spatial resources required for handling operations. This could maximise terminal efficiency and minimise terminal costs, hence improving terminal competitiveness. Moreover, the interpretation of the discovered knowledge, made it possible to evaluate the most discriminating variables of the analysis, even thanks to graphical visualisation of the Importance-plots

MODELLING CONTAINER LOGISTICS PROCESSES IN CONTAINER TERMINALS: A CASE STUDY IN ALEXANDRIA

This study aims to optimize the logistics processes of container terminals. Potentially powerful pipe-flow models of container terminal logistics processes have been neglected to date and modelling of terminals is rare. Because research which adopts a pipe flow and dynamic operational perspective is rare, a case application in Alexandria, Egypt collated empirical container and information flows using interviews and company records to describe its logistics processes and model container and information flows. The methodology used includes qualitative and quantitative methods and a descriptive methodology proceeds sequentially. Primary and secondary data were presented as a pipe flow model to show interrelations between the company’s resources and to identify bottlenecks. Simulation modelling used Simul8 software. Operational level modelling of both import and export flows simulated the actual inbound and outbound flows of containers from entry to exit. The import logistics process includes activities such as unloading vessels by quay cranes, moving containers by tractors to yard cranes to go for storage where customs procedures take place before exiting the terminal by customer’s truck. The export logistics process includes the activities associated with customers’ trucks, lifters, storage yards, tractors and quay cranes. The model takes into account the uncertainties in each activity. This study focuses on operational aspects rather than cost issues, and considers container flows rather than vessel flows. Although the simulated model was not generalized, implementation elsewhere is possible. Following successful validation of a base simulation model which reproduces the case company’s historical scenario, scenario testing empowered the case company to pro-actively design and test the impact of operational changes on the entire logistics process. The study evaluates a typical container terminal logistics system including both import and export containers in the presence of multiple uncertainties in terminal operations (e.g. quay crane operations, tractor operations, yard crane operations). Sensitivity testing and scenario analysis can empower terminal managers to make decisions to improve performance, and to guide terminal planners, managers, and operators in testing future investment scenarios before implementation.Arab Academy for Science, Technology and Maritime Transpor

Optimization of yard operations in container terminals from an energy efficiency approach

This Thesis addresses common operational issues related to maritime container terminals. In the last decades, containerization of maritime transportation has grown very rapidly, forcing terminal operators to cope with unprecedented volumes of containers in a continuous manner. As a consequence, terminal efficiency is always a critical factor. In the near future, operators are also expected to face increasing operational costs deriving firstly from the energy crisis and secondly from new regulations enforcing ports to become more environmentally friendly. As a consequence, operational inefficiencies deriving from periods of congestion require innovative solutions and optimization techniques to improve the efficiency and productivity in the terminal yard. This Thesis addresses such problems by introducing an electric energy consumption model that characterizes energy expenditure of yard cranes. For each gantry, trolley and hoist movement of the cranes, the model takes into account the different resistances that must be overcome during the acceleration, constant speed and deceleration phases of each movement. The energy consumption model is coupled to two different discrete event simulation models of one parallel and one perpendicular container terminals, with the goal to analyze the handling operations and optimize energy efficiency and productivity. One additional innovative aspect of the works is that they include the effect of the volume of container traffic in the analysis with the aim to assess differences in the performance of the algorithms under a range of realistic scenarios, which is usually neglected in similar studies. Finally, in addition to stacking and retrieval operations, the works also introduce housekeeping operation, which are common in the real world but often disregarded in the literature. Such operations are relevant as they may be critical in terms of achieving good productivity, but on the other hand they amount for a significant portion of the overall energy consumption. In particular, the works of the Thesis deal have four particular objectives: (1) providing such flexible and customizable numerical models of discrete event type to simulate and analyze parallel and perpendicular terminals, (2) proposing a new stacking algorithm to reduce energy expenditure and improve automatic stacking crane productivity in perpendicular terminals; (3) optimizing the dimensions of a perpendicular layout; and (4) analyzing the distribution of containers in the yard layout as a function of the moment at which space for export containers is reserved while looking at the operational costs. In the first place, results show the models are capable of characterizing in detail the energy consumption associated to crane movements in both parallel and perpendicular terminals. With respect to perpendicular terminals, the proposed stacking algorithm is capable of improving the energy efficiency up to around 20% while achieving greater productivity at the same time. In addition, results show that the dimensions of a perpendicular terminal block can be optimized so as to improve the productivity; with respect to energy consumption, although a smaller block induces lesser electrical consumption, the random nature of housekeeping operations produce a significant degree of distortion in the results, revealing that such operations constitute a promising flied for future research. Finally, considering parallel terminals, a greater degree of clustering is observed as the reservation is made earlier. When considering the associated operational costs associated to yard cranes and yard trucks, greater clustering results in more efficient use of the energy, and therefore reservation may be desirable when possible to enhance terminal productivity.Esta Tesis aborda temas operativos comunes relacionados con terminales marítimas de contenedores. En las últimas décadas, la contenerización del transporte marítimo ha crecido exponencialmente, obligando a los operadores a hacer frente a volúmenes de contenedores sin precedentes de manera continuada. Como consecuencia, la eficiencia de las operaciones es siempre un factor crítico. En un futuro próximo, los operadores también deberán afrontar crecientes costes operativos derivados de la crisis energética, y también de nuevas regulaciones que obligan a los puertos a volverse más respetuosos con el medio ambiente. Por estos motivos, las ineficiencias operativas derivadas de períodos de congestión requieren soluciones innovadoras y técnicas de optimización para mejorar la eficiencia y productividad en los patios de contenedores. Esta tesis aborda estos problemas introduciendo un modelo de consumo de energía eléctrica que caracteriza el gasto de las grúas de patio. Para cada movimiento de "gantry", "hoist" y "spreader", el modelo tiene en cuenta las diferentes resistencias que deben superarse durante las fases de aceleración, velocidad constante y deceleración del movimiento. El modelo de consumo de energía se ha acoplado a dos modelos de simulación de eventos discretos de terminales de contenedores, una paralela y otra perpendicular, con el objetivo de analizar las operaciones de manipulación y optimizar la eficiencia energética y la productividad. Otro aspecto innovador de este trabajo es que analiza el efecto del volumen de tráfico de contenedores con el objetivo de evaluar el comportamiento de los algoritmos bajo un rango de escenarios realistas, lo que generalmente no se tiene en cuenta en estudios similares. Por último, además de las operaciones de apilamiento y salida de contenedores, la tesis también considera las operaciones de reordenamiento del patio, muy comunes en el mundo real, pero que a menudo no se tienen en cuenta en la literatura. Tales operaciones pueden ser críticas para lograr una buena productividad, pero por otra parte representan una parte importante del consumo total de energía. En particular, los trabajos desarrollados en esta Tesis tienen cuatro objetivos concretos: (1) proporcionar modelos numéricos flexibles y configurables de tipo eventos discretos para simular y analizar terminales paralelas y perpendiculares, (2) proponer un nuevo algoritmo de apilamiento para reducir el gasto de energía y mejorar la productividad de la grúa automático en terminales perpendiculares; (3) optimizar las dimensiones de un bloque de una terminal perpendicular; y (4) analizar la distribución de los contenedores en la disposición del patio en función del momento en que se reserva el espacio para los contenedores de exportación. Los resultados muestran que, en primer lugar, los modelos son capaces de caracterizar en detalle el consumo de energía asociado a los movimientos de las grúas en ambos tipos de terminales. Con respecto a las terminales perpendiculares, el algoritmo de apilado propuesto es capaz de mejorar la eficiencia energética hasta aproximadamente un 20%, al tiempo que se consigue una mayor productividad. Además, los resultados muestran que las dimensiones de un bloque perpendicular pueden optimizarse para mejorar la productividad; con respecto al consumo de energía, aunque un bloque más pequeño induce un menor consumo eléctrico, la naturaleza aleatoria de las operaciones de reordenación inducen un grado significativo de distorsión en los resultados, indicando que tales operaciones pueden ser objeto de futura investigación. Por último, respecto a las terminales paralelas, a medida que se adelanta la reserva de espacio los contenedores presentan un mayor grado de agrupación, lo que redunda en un uso más eficeficiente de la energía debido a los menores costos operacionales asociados a grúas y camiones de patio, por lo que la reserva puede ser aconsejable cuando sea posible para mejorar la productividad del termina

AIRO 2016. 46th Annual Conference of the Italian Operational Research Society. Emerging Advances in Logistics Systems Trieste, September 6-9, 2016 - Abstracts Book

The AIRO 2016 book of abstract collects the contributions from the conference participants. The AIRO 2016 Conference is a special occasion for the Italian Operations Research community, as AIRO annual conferences turn 46th edition in 2016. To reflect this special occasion, the Programme and Organizing Committee, chaired by Walter Ukovich, prepared a high quality Scientific Programme including the first initiative of AIRO Young, the new AIRO poster section that aims to promote the work of students, PhD students, and Postdocs with an interest in Operations Research. The Scientific Programme of the Conference offers a broad spectrum of contributions covering the variety of OR topics and research areas with an emphasis on “Emerging Advances in Logistics Systems”. The event aims at stimulating integration of existing methods and systems, fostering communication amongst different research groups, and laying the foundations for OR integrated research projects in the next decade. Distinct thematic sections follow the AIRO 2016 days starting by initial presentation of the objectives and features of the Conference. In addition three invited internationally known speakers will present Plenary Lectures, by Gianni Di Pillo, Frédéric Semet e Stefan Nickel, gathering AIRO 2016 participants together to offer key presentations on the latest advances and developments in OR’s research

Integrated vehicle dispatching for container terminal

Ph.DDOCTOR OF PHILOSOPH