Managing the Congestion for Delivering and Receiving Truck Container at the Tanjung Priok Terminal by Analyzing the Congestion at Koja Container Terminal

This study aimed to analyze the congestion that occurred around the Koja Container Terminal to develop strategies that will aid in managing the congestion presently occurring at the Tanjung Priok Container Terminal. Dwelling time, ship delay, gate server down, equipment damage, and lack of support for the truck arrival system were some causes of congestion in the Tanjung Priok area. There was an improvement in the truck arrival system (TAS) and Chassis Exchange Terminal (CET). Analyzing the inefficiency in managing the arrival of trucks for container pickup is the strategy needed for controlling congestion. This research was conducted by observing the field movements at the Koja Container Terminal and interviewing the people who experienced congestion in the terminal. This methodology was proposed to aid in efficiently operating the Tanjung Priok Port and alleviate truck congestion. Because it provides systematic, structured, and problem-solving benefits, this study is expected to be a source of consideration for stakeholders when making decisions. The result of this study will help improve the management of congestion at Terminal Koja. Keywords: Managing Congestion, Delivering and Receiving, Truck Container, Terminal Container, Por

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

Agent-based Truck Appointment System for Containers Pick-up Time Negotiation

Congestion in the seaports area is a common issue in many parts of the world. Fluctuating truck arrival has been identified as one of the significant determinants of congestion. In response, a truck appointment system (TAS) is introduced to manage truck arrival, particularly at peak times. In the existing TAS mechanism, the scheduling decision is centralized and disregards the concerns of trucking companies. Moreover, TAS may complicate the business operation of trucking companies that already have a constrained truck schedule. This study proposes a decentralized negotiation mechanism in TAS that allows trucking companies to adjust arrival times by utilizing the waiting time estimation provided by the terminal operator. We develop an agent-based model of a TAS in the container terminal pick-up procedure. The simulation results indicate that compared to the existing TAS mechanism, the negotiation TAS mechanism generates a shorter average truck turnaround time regardless of truck arrival rates. In terms of average net time cost, the negotiation TAS mechanism provides better value under high truck arrival rate conditions. The incentive for trucking companies to participate in the negotiations is even higher at peak times

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

Pla de Doctorat Industrial de la Generalitat de CatalunyaThis 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 terminalPostprint (published version

The stochastic container relocation problem with flexible service policies

This paper investigates the Stochastic Container Relocation Problem in which a flexible service policy is adopted in the import container retrieval process. The flexible policy allows the terminal operators to determine the container retrieval sequence to some extent, which provides more opportunity for reducing the number of relocations and the truck waiting times. A more general probabilistic model that captures customers’ arrival preference is presented to describe the randomness for external truck arrivals within their appointed time windows. Being a multi-stage stochastic sequential decision-making problem, it is first formulated into a stochastic dynamic programming (SDP) model to minimize the expected number of relocations. Then, the SDP model is extended considering a secondary objective representing the truck waiting times. Tree search-based algorithms are adapted to solve the two models to their optimality. Heuristic algorithms are designed to seek high-quality solutions efficiently for larger problems. A discrete-event simulation model is developed to evaluate the optimal solutions and the heuristic solutions respectively on two performance metrics. Extensive computational experiments are performed based on instances from literature to verify the effectiveness of the proposed models and algorithms

Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals

Tesis por compendioDespite the continuous evolution in computers and information technology, real-world combinatorial optimization problems are NP-problems, in particular in the domain of planning and scheduling. Thus, although exact techniques from the Operations Research (OR) field, such as Linear Programming, could be applied to solve optimization problems, they are difficult to apply in real-world scenarios since they usually require too much computational time, i.e: an optimized solution is required at an affordable computational time. Furthermore, decision makers often face different and typically opposing goals, then resulting multi-objective optimization problems. Therefore, approximate techniques from the Artificial Intelligence (AI) field are commonly used to solve the real world problems. The AI techniques provide richer and more flexible representations of real-world (Gomes 2000), and they are widely used to solve these type of problems. AI heuristic techniques do not guarantee the optimal solution, but they provide near-optimal solutions in a reasonable time. These techniques are divided into two broad classes of algorithms: constructive and local search methods (Aarts and Lenstra 2003). They can guide their search processes by means of heuristics or metaheuristics depending on how they escape from local optima (Blum and Roli 2003). Regarding multi-objective optimization problems, the use of AI techniques becomes paramount due to their complexity (Coello Coello 2006). Nowadays, the point of view for planning and scheduling tasks has changed. Due to the fact that real world is uncertain, imprecise and non-deterministic, there might be unknown information, breakdowns, incidences or changes, which become the initial plans or schedules invalid. Thus, there is a new trend to cope these aspects in the optimization techniques, and to seek robust solutions (schedules) (Lambrechts, Demeulemeester, and Herroelen 2008). In this way, these optimization problems become harder since a new objective function (robustness measure) must be taken into account during the solution search. Therefore, the robustness concept is being studied and a general robustness measure has been developed for any scheduling problem (such as Job Shop Problem, Open Shop Problem, Railway Scheduling or Vehicle Routing Problem). To this end, in this thesis, some techniques have been developed to improve the search of optimized and robust solutions in planning and scheduling problems. These techniques offer assistance to decision makers to help in planning and scheduling tasks, determine the consequences of changes, provide support in the resolution of incidents, provide alternative plans, etc. As a case study to evaluate the behaviour of the techniques developed, this thesis focuses on problems related to container terminals. Container terminals generally serve as a transshipment zone between ships and land vehicles (trains or trucks). In (Henesey 2006a), it is shown how this transshipment market has grown rapidly. Container terminals are open systems with three distinguishable areas: the berth area, the storage yard, and the terminal receipt and delivery gate area. Each one presents different planning and scheduling problems to be optimized (Stahlbock and Voß 2008). For example, berth allocation, quay crane assignment, stowage planning, and quay crane scheduling must be managed in the berthing area; the container stacking problem, yard crane scheduling, and horizontal transport operations must be carried out in the yard area; and the hinterland operations must be solved in the landside area. Furthermore, dynamism is also present in container terminals. The tasks of the container terminals take place in an environment susceptible of breakdowns or incidences. For instance, a Quay Crane engine stopped working and needs to be revised, delaying this task one or two hours. Thereby, the robustness concept can be included in the scheduling techniques to take into consideration some incidences and return a set of robust schedules. In this thesis, we have developed a new domain-dependent planner to obtain more effi- cient solutions in the generic problem of reshuffles of containers. Planning heuristics and optimization criteria developed have been evaluated on realistic problems and they are applicable to the general problem of reshuffling in blocks world scenarios. Additionally, we have developed a scheduling model, using constructive metaheuristic techniques on a complex problem that combines sequences of scenarios with different types of resources (Berth Allocation, Quay Crane Assignment, and Container Stacking problems). These problems are usually solved separately and their integration allows more optimized solutions. Moreover, in order to address the impact and changes that arise in dynamic real-world environments, a robustness model has been developed for scheduling tasks. This model has been applied to metaheuristic schemes, which are based on genetic algorithms. The extension of such schemes, incorporating the robustness model developed, allows us to evaluate and obtain more robust solutions. This approach, combined with the classical optimality criterion in scheduling problems, allows us to obtain, in an efficient in way, optimized solution able to withstand a greater degree of incidents that occur in dynamic scenarios. Thus, a proactive approach is applied to the problem that arises with the presence of incidences and changes that occur in typical scheduling problems of a dynamic real world.Rodríguez Molins, M. (2015). Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48545TESISCompendi

Analysis of marine container terminal gate congestion, truck waiting cost, and system optimization

As world container volume continues to grow and the introduction of 12,000 TEUs plus containerships into major trade routes, the port industry is under pressure to deal with the ever increasing freight volume. Gate congestion at marine container terminal is considered a major issue facing truckers who come to the terminal for container pickup and delivery. Harbor truckers operate in a very competitive environment; they are paid by trip, not by the hours they drive. Gate congestion is not only detrimental to their economic well-being, but also causes environmental pollution. This thesis applies a multi-server queuing model to analyze marine terminal gate congestion and quantify truck waiting cost. In addition, an optimization model is developed to minimize gate system cost. Extensive data collection includes field observations and online camera observation and terminal day-to-day operation records. Comprehensive data analysis provides a solid foundation to support the development of the optimization model. The queuing analysis indicates that there is a substantial truck waiting cost incurred during peak season. Three optimization alternatives are explored. The results prove that optimization by appointment is the most effective way to reduce gate congestion and improve system efficiency. Lastly, it is the recommendation to use the combination of optimization by appointment and productivity improvement to mitigate terminal gate congestion and accommodate the ever growing container volume